diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json index 10b39e52a2..0d507b72f1 100644 --- a/dev/.documenter-siteinfo.json +++ b/dev/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.9.3","generation_timestamp":"2023-11-07T21:03:46","documenter_version":"1.1.2"}} \ No newline at end of file +{"documenter":{"julia_version":"1.9.3","generation_timestamp":"2023-11-07T22:34:27","documenter_version":"1.1.2"}} \ No newline at end of file diff --git a/dev/api/index.html b/dev/api/index.html index d8d3969f02..83883fa506 100644 --- a/dev/api/index.html +++ b/dev/api/index.html @@ -1,40 +1,40 @@ -API reference · DFTK.jl

API reference

This page provides a plain list of all documented functions, structs, modules and macros in DFTK. Note that this list is neither structured, complete nor particularly clean, so it only provides rough orientation at the moment. The best reference is the code itself.

DFTK.DFTKModule

DFTK –- The density-functional toolkit. Provides functionality for experimenting with plane-wave density-functional theory algorithms.

source
DFTK.AdaptiveBandsType

Dynamically adapt number of bands to be converged to ensure that the orbitals of lowest occupation are occupied to at most occupation_threshold. To obtain rapid convergence of the eigensolver a gap between the eigenvalues of the last occupied orbital and the last computed (but not converged) orbital of gap_min is ensured.

source
DFTK.AtomicNonlocalType

Nonlocal term coming from norm-conserving pseudopotentials in Kleinmann-Bylander form. $\text{Energy} = \sum_a \sum_{ij} \sum_{n} f_n <ψ_n|p_{ai}> D_{ij} <p_{aj}|ψ_n>.$

source
DFTK.BlowupCHVType

Blow-up function as proposed in https://arxiv.org/abs/2210.00442 The blow-up order of the function is fixed to ensure C^2 regularity of the energies bands away from crossings and Lipschitz continuity at crossings.

source
DFTK.DielectricMixingType

We use a simplification of the Resta model DOI 10.1103/physrevb.16.2717 and set $χ_0(q) = \frac{C_0 G^2}{4π (1 - C_0 G^2 / k_{TF}^2)}$ where $C_0 = 1 - ε_r$ with $ε_r$ being the macroscopic relative permittivity. We neglect $K_\text{xc}$, such that $J^{-1} ≈ \frac{k_{TF}^2 - C_0 G^2}{ε_r k_{TF}^2 - C_0 G^2}$

By default it assumes a relative permittivity of 10 (similar to Silicon). εr == 1 is equal to SimpleMixing and εr == Inf to KerkerMixing. The mixing is applied to $ρ$ and $ρ_\text{spin}$ in the same way.

source
DFTK.DielectricModelType

A localised dielectric model for $χ_0$:

\[\sqrt{L(x)} \text{IFFT} \frac{C_0 G^2}{4π (1 - C_0 G^2 / k_{TF}^2)} \text{FFT} \sqrt{L(x)}\]

where $C_0 = 1 - ε_r$, L(r) is a real-space localization function and otherwise the same conventions are used as in DielectricMixing.

source
DFTK.DivAgradOperatorType

Nonlocal "divAgrad" operator $-½ ∇ ⋅ (A ∇)$ where $A$ is a scalar field on the real-space grid. The $-½$ is included, such that this operator is a generalisation of the kinetic energy operator (which is obtained for $A=1$).

source
DFTK.ElementCohenBergstresserMethod

Element where the interaction with electrons is modelled as in CohenBergstresser1966. Only the homonuclear lattices of the diamond structure are implemented (i.e. Si, Ge, Sn).

key may be an element symbol (like :Si), an atomic number (e.g. 14) or an element name (e.g. "silicon")

source
DFTK.ElementCoulombMethod

Element interacting with electrons via a bare Coulomb potential (for all-electron calculations) key may be an element symbol (like :Si), an atomic number (e.g. 14) or an element name (e.g. "silicon")

source
DFTK.ElementPspMethod

Element interacting with electrons via a pseudopotential model. key may be an element symbol (like :Si), an atomic number (e.g. 14) or an element name (e.g. "silicon")

source
DFTK.EnergiesType

A simple struct to contain a vector of energies, and utilities to print them in a nice format.

source
DFTK.EntropyType

Entropy term -TS, where S is the electronic entropy. Turns the energy E into the free energy F=E-TS. This is in particular useful because the free energy, not the energy, is minimized at self-consistency.

source
DFTK.EwaldType

Ewald term: electrostatic energy per unit cell of the array of point charges defined by model.atoms in a uniform background of compensating charge yielding net neutrality.

source
DFTK.ExternalFromRealType

External potential from an analytic function V (in cartesian coordinates). No low-pass filtering is performed.

source
DFTK.FixedBandsType

In each SCF step converge exactly n_bands_converge, computing along the way exactly n_bands_compute (usually a few more to ease convergence in systems with small gaps).

source
DFTK.GPUMethod

Construct a particular GPU architecture by passing the ArrayType

source
DFTK.HartreeType

Hartree term: for a decaying potential V the energy would be

1/2 ∫ρ(x)ρ(y)V(x-y) dxdy

with the integral on x in the unit cell and of y in the whole space. For the Coulomb potential with periodic boundary conditions, this is rather

1/2 ∫ρ(x)ρ(y) G(x-y) dx dy

where G is the Green's function of the periodic Laplacian with zero mean (-Δ G = sum{R} 4π δR, integral of G zero on a unit cell).

source
DFTK.KerkerMixingType

Kerker mixing: $J^{-1} ≈ \frac{|G|^2}{k_{TF}^2 + |G|^2}$ where $k_{TF}$ is the Thomas-Fermi wave vector. For spin-polarized calculations by default the spin density is not preconditioned. Unless a non-default value for $ΔDOS_Ω$ is specified. This value should roughly be the expected difference in density of states (per unit volume) between spin-up and spin-down.

Notes:

  • Abinit calls $1/k_{TF}$ the dielectric screening length (parameter dielng)
source
DFTK.KpointType

Discretization information for $k$-point-dependent quantities such as orbitals. More generally, a $k$-point is a block of the Hamiltonian; eg collinear spin is treated by doubling the number of kpoints.

source
DFTK.LazyHcatType

Simple wrapper to represent a matrix formed by the concatenation of column blocks: it is mostly equivalent to hcat, but doesn't allocate the full matrix. LazyHcat only supports a few multiplication routines: furthermore, a multiplication involving this structure will always yield a plain array (and not a LazyHcat structure). LazyHcat is a lightweight subset of BlockArrays.jl's functionalities, but has the advantage to be able to store GPU Arrays (BlockArrays is heavily built on Julia's CPU Array).

source
DFTK.LdosModelType

Represents the LDOS-based $χ_0$ model

\[χ_0(r, r') = (-D_\text{loc}(r) δ(r, r') + D_\text{loc}(r) D_\text{loc}(r') / D)\]

where $D_\text{loc}$ is the local density of states and $D$ the density of states. For details see Herbst, Levitt 2020 arXiv:2009.01665

source
DFTK.MagneticType

Magnetic term $A⋅(-i∇)$. It is assumed (but not checked) that $∇⋅A = 0$.

source
DFTK.ModelMethod
Model(system::AbstractSystem; kwargs...)

AtomsBase-compatible Model constructor. Sets structural information (atoms, positions, lattice, n_electrons etc.) from the passed system.

source
DFTK.ModelMethod
Model(lattice, atoms, positions; n_electrons, magnetic_moments, terms, temperature,
-      smearing, spin_polarization, symmetries)

Creates the physical specification of a model (without any discretization information).

n_electrons is taken from atoms if not specified.

spin_polarization is :none by default (paired electrons) unless any of the elements has a non-zero initial magnetic moment. In this case the spin_polarization will be :collinear.

magnetic_moments is only used to determine the symmetry and the spin_polarization; it is not stored inside the datastructure.

smearing is Fermi-Dirac if temperature is non-zero, none otherwise

The symmetries kwarg allows (a) to pass true / false to enable / disable the automatic determination of lattice symmetries or (b) to pass an explicit list of symmetry operations to use for lowering the computational effort. The default behaviour is equal to true, namely that the code checks the specified model in form of the Hamiltonian terms, lattice, atoms and magnetic_moments parameters and from these automatically determines a set of symmetries it can safely use. If you want to pass custom symmetry operations (e.g. a reduced or extended set) use the symmetry_operations function. Notice that this may lead to wrong results if e.g. the external potential breaks some of the passed symmetries. Use false to turn off symmetries completely.

source
DFTK.ModelMethod
Model(model; [lattice, positions, atoms, kwargs...])
-Model{T}(model; [lattice, positions, atoms, kwargs...])

Construct an identical model to model with the option to change some of the contained parameters. This constructor is useful for changing the data type in the model or for changing lattice or positions in geometry/lattice optimisations.

source
DFTK.NonlocalOperatorType

Nonlocal operator in Fourier space in Kleinman-Bylander format, defined by its projectors P matrix and coupling terms D: Hψ = PDP' ψ.

source
DFTK.NoopOperatorType

Noop operation: don't do anything. Useful for energy terms that don't depend on the orbitals at all (eg nuclei-nuclei interaction).

source
DFTK.PairwisePotentialMethod

Pairwise terms: Pairwise potential between nuclei, e.g., Van der Waals potentials, such as Lennard—Jones terms. The potential is dependent on the distance between to atomic positions and the pairwise atomic types: For a distance d between to atoms A and B, the potential is V(d, params[(A, B)]). The parameters max_radius is of 100 by default, and gives the maximum distance (in Cartesian coordinates) between nuclei for which we consider interactions.

source
DFTK.PlaneWaveBasisType

A plane-wave discretized Model. Normalization conventions:

  • Things that are expressed in the G basis are normalized so that if $x$ is the vector, then the actual function is $\sum_G x_G e_G$ with $e_G(x) = e^{iG x} / \sqrt(\Omega)$, where $\Omega$ is the unit cell volume. This is so that, eg $norm(ψ) = 1$ gives the correct normalization. This also holds for the density and the potentials.
  • Quantities expressed on the real-space grid are in actual values.

ifft and fft convert between these representations.

source
DFTK.PlaneWaveBasisMethod

Creates a PlaneWaveBasis using the kinetic energy cutoff Ecut and a Monkhorst-Pack $k$-point grid. The MP grid can either be specified directly with kgrid providing the number of points in each dimension and kshift the shift (0 or 1/2 in each direction). If not specified a grid is generated using kgrid_from_minimal_spacing with a minimal spacing of 2π * 0.022 per Bohr.

source
DFTK.PreconditionerTPAType

(simplified version of) Tetter-Payne-Allan preconditioning ↑ M.P. Teter, M.C. Payne and D.C. Allan, Phys. Rev. B 40, 12255 (1989).

source
DFTK.PspHghMethod
PspHgh(path[, identifier, description])

Construct a Hartwigsen, Goedecker, Teter, Hutter separable dual-space Gaussian pseudopotential (1998) from file.

source
DFTK.PspUpfMethod
PspUpf(path[, identifier])

Construct a Unified Pseudopotential Format pseudopotential from file.

Does not support:

  • Non-linear core correction
  • Fully-realtivistic / spin-orbit pseudos
  • Bare Coulomb / all-electron potentials
  • Semilocal potentials
  • Ultrasoft potentials
  • Projector-augmented wave potentials
  • GIPAW reconstruction data
source
DFTK.RealFourierOperatorType

Linear operators that act on tuples (real, fourier) The main entry point is apply!(out, op, in) which performs the operation out += op*in where out and in are named tuples (real, fourier) They also implement mul! and Matrix(op) for exploratory use.

source
DFTK.XcType

Exchange-correlation term, defined by a list of functionals and usually evaluated through libxc.

source
DFTK.χ0MixingType

Generic mixing function using a model for the susceptibility composed of the sum of the χ0terms. For valid χ0terms See the subtypes of χ0Model. The dielectric model is solved in real space using a GMRES. Either the full kernel (RPA=false) or only the Hartree kernel (RPA=true) are employed. verbose=true lets the GMRES run in verbose mode (useful for debugging).

source
AbstractFFTs.fft!Method

In-place version of fft!. NOTE: If kpt is given, not only f_fourier but also f_real is overwritten.

source
AbstractFFTs.fftMethod
fft(basis::PlaneWaveBasis, [kpt::Kpoint, ] f_real)

Perform an FFT to obtain the Fourier representation of f_real. If kpt is given, the coefficients are truncated to the k-dependent spherical basis set.

source
AbstractFFTs.ifftMethod
ifft(basis::PlaneWaveBasis, [kpt::Kpoint, ] f_fourier)

Perform an iFFT to obtain the quantity defined by f_fourier defined on the k-dependent spherical basis set (if kpt is given) or the k-independent cubic (if it is not) on the real-space grid.

source
AtomsBase.atomic_systemFunction
atomic_system(model::DFTK.Model, magnetic_moments=[])
-atomic_system(lattice, atoms, positions, magnetic_moments=[])

Construct an AtomsBase atomic system from a DFTK model and associated magnetic moments or from the usual lattice, atoms and positions list used in DFTK plus magnetic moments.

source
AtomsBase.periodic_systemFunction
periodic_system(model::DFTK.Model, magnetic_moments=[])
-periodic_system(lattice, atoms, positions, magnetic_moments=[])

Construct an AtomsBase atomic system from a DFTK model and associated magnetic moments or from the usual lattice, atoms and positions list used in DFTK plus magnetic moments.

source
Brillouin.KPaths.irrfbz_pathMethod

Extract the high-symmetry $k$-point path corresponding to the passed model using Brillouin. Uses the conventions described in the reference work by Cracknell, Davies, Miller, and Love (CDML). Of note, this has minor differences to the $k$-path reference (Y. Himuma et. al. Comput. Mater. Sci. 128, 140 (2017)) underlying the path-choices of Brillouin.jl, specifically for oA and mC Bravais types.

If the cell is a supercell of a smaller primitive cell, the standard $k$-path of the associated primitive cell is returned. So, the high-symmetry $k$ points are those of the primitive cell Brillouin zone, not those of the supercell Brillouin zone.

The dim argument allows to artificially truncate the dimension of the employed model, e.g. allowing to plot a 2D bandstructure of a 3D model (useful for example for plotting band structures of sheets with dim=2).

Due to lacking support in Spglib.jl for two-dimensional lattices it is (a) assumed that model.lattice is a conventional lattice and (b) required to pass the space group number using the sgnum keyword argument.

source
DFTK.CROPFunction

CROP-accelerated root-finding iteration for f, starting from x0 and keeping a history of m steps. Optionally warming specifies the number of non-accelerated steps to perform for warming up the history.

source
DFTK.G_vectorsMethod
G_vectors(basis::PlaneWaveBasis)
-G_vectors(basis::PlaneWaveBasis, kpt::Kpoint)

The list of wave vectors $G$ in reduced (integer) coordinates of a basis or a $k$-point kpt.

source
DFTK.G_vectorsMethod
G_vectors([architecture=AbstractArchitecture], fft_size::Tuple)

The wave vectors G in reduced (integer) coordinates for a cubic basis set of given sizes.

source
DFTK.G_vectors_cartMethod
G_vectors_cart(basis::PlaneWaveBasis)
-G_vectors_cart(basis::PlaneWaveBasis, kpt::Kpoint)

The list of $G$ vectors of a given basis or kpt, in cartesian coordinates.

source
DFTK.Gplusk_vectorsMethod
Gplusk_vectors(basis::PlaneWaveBasis, kpt::Kpoint)

The list of $G + k$ vectors, in reduced coordinates.

source
DFTK.Gplusk_vectors_cartMethod
Gplusk_vectors_cart(basis::PlaneWaveBasis, kpt::Kpoint)

The list of $G + k$ vectors, in cartesian coordinates.

source
DFTK.HybridMixingMethod

The model for the susceptibility is

\[\begin{aligned} +API reference · DFTK.jl

API reference

This page provides a plain list of all documented functions, structs, modules and macros in DFTK. Note that this list is neither structured, complete nor particularly clean, so it only provides rough orientation at the moment. The best reference is the code itself.

DFTK.DFTKModule

DFTK –- The density-functional toolkit. Provides functionality for experimenting with plane-wave density-functional theory algorithms.

source
DFTK.AdaptiveBandsType

Dynamically adapt number of bands to be converged to ensure that the orbitals of lowest occupation are occupied to at most occupation_threshold. To obtain rapid convergence of the eigensolver a gap between the eigenvalues of the last occupied orbital and the last computed (but not converged) orbital of gap_min is ensured.

source
DFTK.AtomicNonlocalType

Nonlocal term coming from norm-conserving pseudopotentials in Kleinmann-Bylander form. $\text{Energy} = \sum_a \sum_{ij} \sum_{n} f_n <ψ_n|p_{ai}> D_{ij} <p_{aj}|ψ_n>.$

source
DFTK.BlowupCHVType

Blow-up function as proposed in https://arxiv.org/abs/2210.00442 The blow-up order of the function is fixed to ensure C^2 regularity of the energies bands away from crossings and Lipschitz continuity at crossings.

source
DFTK.DielectricMixingType

We use a simplification of the Resta model DOI 10.1103/physrevb.16.2717 and set $χ_0(q) = \frac{C_0 G^2}{4π (1 - C_0 G^2 / k_{TF}^2)}$ where $C_0 = 1 - ε_r$ with $ε_r$ being the macroscopic relative permittivity. We neglect $K_\text{xc}$, such that $J^{-1} ≈ \frac{k_{TF}^2 - C_0 G^2}{ε_r k_{TF}^2 - C_0 G^2}$

By default it assumes a relative permittivity of 10 (similar to Silicon). εr == 1 is equal to SimpleMixing and εr == Inf to KerkerMixing. The mixing is applied to $ρ$ and $ρ_\text{spin}$ in the same way.

source
DFTK.DielectricModelType

A localised dielectric model for $χ_0$:

\[\sqrt{L(x)} \text{IFFT} \frac{C_0 G^2}{4π (1 - C_0 G^2 / k_{TF}^2)} \text{FFT} \sqrt{L(x)}\]

where $C_0 = 1 - ε_r$, L(r) is a real-space localization function and otherwise the same conventions are used as in DielectricMixing.

source
DFTK.DivAgradOperatorType

Nonlocal "divAgrad" operator $-½ ∇ ⋅ (A ∇)$ where $A$ is a scalar field on the real-space grid. The $-½$ is included, such that this operator is a generalisation of the kinetic energy operator (which is obtained for $A=1$).

source
DFTK.ElementCohenBergstresserMethod

Element where the interaction with electrons is modelled as in CohenBergstresser1966. Only the homonuclear lattices of the diamond structure are implemented (i.e. Si, Ge, Sn).

key may be an element symbol (like :Si), an atomic number (e.g. 14) or an element name (e.g. "silicon")

source
DFTK.ElementCoulombMethod

Element interacting with electrons via a bare Coulomb potential (for all-electron calculations) key may be an element symbol (like :Si), an atomic number (e.g. 14) or an element name (e.g. "silicon")

source
DFTK.ElementPspMethod

Element interacting with electrons via a pseudopotential model. key may be an element symbol (like :Si), an atomic number (e.g. 14) or an element name (e.g. "silicon")

source
DFTK.EnergiesType

A simple struct to contain a vector of energies, and utilities to print them in a nice format.

source
DFTK.EntropyType

Entropy term -TS, where S is the electronic entropy. Turns the energy E into the free energy F=E-TS. This is in particular useful because the free energy, not the energy, is minimized at self-consistency.

source
DFTK.EwaldType

Ewald term: electrostatic energy per unit cell of the array of point charges defined by model.atoms in a uniform background of compensating charge yielding net neutrality.

source
DFTK.ExternalFromRealType

External potential from an analytic function V (in cartesian coordinates). No low-pass filtering is performed.

source
DFTK.FixedBandsType

In each SCF step converge exactly n_bands_converge, computing along the way exactly n_bands_compute (usually a few more to ease convergence in systems with small gaps).

source
DFTK.GPUMethod

Construct a particular GPU architecture by passing the ArrayType

source
DFTK.HartreeType

Hartree term: for a decaying potential V the energy would be

1/2 ∫ρ(x)ρ(y)V(x-y) dxdy

with the integral on x in the unit cell and of y in the whole space. For the Coulomb potential with periodic boundary conditions, this is rather

1/2 ∫ρ(x)ρ(y) G(x-y) dx dy

where G is the Green's function of the periodic Laplacian with zero mean (-Δ G = sum{R} 4π δR, integral of G zero on a unit cell).

source
DFTK.KerkerMixingType

Kerker mixing: $J^{-1} ≈ \frac{|G|^2}{k_{TF}^2 + |G|^2}$ where $k_{TF}$ is the Thomas-Fermi wave vector. For spin-polarized calculations by default the spin density is not preconditioned. Unless a non-default value for $ΔDOS_Ω$ is specified. This value should roughly be the expected difference in density of states (per unit volume) between spin-up and spin-down.

Notes:

  • Abinit calls $1/k_{TF}$ the dielectric screening length (parameter dielng)
source
DFTK.KpointType

Discretization information for $k$-point-dependent quantities such as orbitals. More generally, a $k$-point is a block of the Hamiltonian; eg collinear spin is treated by doubling the number of kpoints.

source
DFTK.LazyHcatType

Simple wrapper to represent a matrix formed by the concatenation of column blocks: it is mostly equivalent to hcat, but doesn't allocate the full matrix. LazyHcat only supports a few multiplication routines: furthermore, a multiplication involving this structure will always yield a plain array (and not a LazyHcat structure). LazyHcat is a lightweight subset of BlockArrays.jl's functionalities, but has the advantage to be able to store GPU Arrays (BlockArrays is heavily built on Julia's CPU Array).

source
DFTK.LdosModelType

Represents the LDOS-based $χ_0$ model

\[χ_0(r, r') = (-D_\text{loc}(r) δ(r, r') + D_\text{loc}(r) D_\text{loc}(r') / D)\]

where $D_\text{loc}$ is the local density of states and $D$ the density of states. For details see Herbst, Levitt 2020 arXiv:2009.01665

source
DFTK.MagneticType

Magnetic term $A⋅(-i∇)$. It is assumed (but not checked) that $∇⋅A = 0$.

source
DFTK.ModelMethod
Model(system::AbstractSystem; kwargs...)

AtomsBase-compatible Model constructor. Sets structural information (atoms, positions, lattice, n_electrons etc.) from the passed system.

source
DFTK.ModelMethod
Model(lattice, atoms, positions; n_electrons, magnetic_moments, terms, temperature,
+      smearing, spin_polarization, symmetries)

Creates the physical specification of a model (without any discretization information).

n_electrons is taken from atoms if not specified.

spin_polarization is :none by default (paired electrons) unless any of the elements has a non-zero initial magnetic moment. In this case the spin_polarization will be :collinear.

magnetic_moments is only used to determine the symmetry and the spin_polarization; it is not stored inside the datastructure.

smearing is Fermi-Dirac if temperature is non-zero, none otherwise

The symmetries kwarg allows (a) to pass true / false to enable / disable the automatic determination of lattice symmetries or (b) to pass an explicit list of symmetry operations to use for lowering the computational effort. The default behaviour is equal to true, namely that the code checks the specified model in form of the Hamiltonian terms, lattice, atoms and magnetic_moments parameters and from these automatically determines a set of symmetries it can safely use. If you want to pass custom symmetry operations (e.g. a reduced or extended set) use the symmetry_operations function. Notice that this may lead to wrong results if e.g. the external potential breaks some of the passed symmetries. Use false to turn off symmetries completely.

source
DFTK.ModelMethod
Model(model; [lattice, positions, atoms, kwargs...])
+Model{T}(model; [lattice, positions, atoms, kwargs...])

Construct an identical model to model with the option to change some of the contained parameters. This constructor is useful for changing the data type in the model or for changing lattice or positions in geometry/lattice optimisations.

source
DFTK.NonlocalOperatorType

Nonlocal operator in Fourier space in Kleinman-Bylander format, defined by its projectors P matrix and coupling terms D: Hψ = PDP' ψ.

source
DFTK.NoopOperatorType

Noop operation: don't do anything. Useful for energy terms that don't depend on the orbitals at all (eg nuclei-nuclei interaction).

source
DFTK.PairwisePotentialMethod

Pairwise terms: Pairwise potential between nuclei, e.g., Van der Waals potentials, such as Lennard—Jones terms. The potential is dependent on the distance between to atomic positions and the pairwise atomic types: For a distance d between to atoms A and B, the potential is V(d, params[(A, B)]). The parameters max_radius is of 100 by default, and gives the maximum distance (in Cartesian coordinates) between nuclei for which we consider interactions.

source
DFTK.PlaneWaveBasisType

A plane-wave discretized Model. Normalization conventions:

  • Things that are expressed in the G basis are normalized so that if $x$ is the vector, then the actual function is $\sum_G x_G e_G$ with $e_G(x) = e^{iG x} / \sqrt(\Omega)$, where $\Omega$ is the unit cell volume. This is so that, eg $norm(ψ) = 1$ gives the correct normalization. This also holds for the density and the potentials.
  • Quantities expressed on the real-space grid are in actual values.

ifft and fft convert between these representations.

source
DFTK.PlaneWaveBasisMethod

Creates a PlaneWaveBasis using the kinetic energy cutoff Ecut and a Monkhorst-Pack $k$-point grid. The MP grid can either be specified directly with kgrid providing the number of points in each dimension and kshift the shift (0 or 1/2 in each direction). If not specified a grid is generated using kgrid_from_minimal_spacing with a minimal spacing of 2π * 0.022 per Bohr.

source
DFTK.PreconditionerTPAType

(simplified version of) Tetter-Payne-Allan preconditioning ↑ M.P. Teter, M.C. Payne and D.C. Allan, Phys. Rev. B 40, 12255 (1989).

source
DFTK.PspHghMethod
PspHgh(path[, identifier, description])

Construct a Hartwigsen, Goedecker, Teter, Hutter separable dual-space Gaussian pseudopotential (1998) from file.

source
DFTK.PspUpfMethod
PspUpf(path[, identifier])

Construct a Unified Pseudopotential Format pseudopotential from file.

Does not support:

  • Non-linear core correction
  • Fully-realtivistic / spin-orbit pseudos
  • Bare Coulomb / all-electron potentials
  • Semilocal potentials
  • Ultrasoft potentials
  • Projector-augmented wave potentials
  • GIPAW reconstruction data
source
DFTK.RealFourierOperatorType

Linear operators that act on tuples (real, fourier) The main entry point is apply!(out, op, in) which performs the operation out += op*in where out and in are named tuples (real, fourier) They also implement mul! and Matrix(op) for exploratory use.

source
DFTK.XcType

Exchange-correlation term, defined by a list of functionals and usually evaluated through libxc.

source
DFTK.χ0MixingType

Generic mixing function using a model for the susceptibility composed of the sum of the χ0terms. For valid χ0terms See the subtypes of χ0Model. The dielectric model is solved in real space using a GMRES. Either the full kernel (RPA=false) or only the Hartree kernel (RPA=true) are employed. verbose=true lets the GMRES run in verbose mode (useful for debugging).

source
AbstractFFTs.fft!Method

In-place version of fft!. NOTE: If kpt is given, not only f_fourier but also f_real is overwritten.

source
AbstractFFTs.fftMethod
fft(basis::PlaneWaveBasis, [kpt::Kpoint, ] f_real)

Perform an FFT to obtain the Fourier representation of f_real. If kpt is given, the coefficients are truncated to the k-dependent spherical basis set.

source
AbstractFFTs.ifftMethod
ifft(basis::PlaneWaveBasis, [kpt::Kpoint, ] f_fourier)

Perform an iFFT to obtain the quantity defined by f_fourier defined on the k-dependent spherical basis set (if kpt is given) or the k-independent cubic (if it is not) on the real-space grid.

source
AtomsBase.atomic_systemFunction
atomic_system(model::DFTK.Model, magnetic_moments=[])
+atomic_system(lattice, atoms, positions, magnetic_moments=[])

Construct an AtomsBase atomic system from a DFTK model and associated magnetic moments or from the usual lattice, atoms and positions list used in DFTK plus magnetic moments.

source
AtomsBase.periodic_systemFunction
periodic_system(model::DFTK.Model, magnetic_moments=[])
+periodic_system(lattice, atoms, positions, magnetic_moments=[])

Construct an AtomsBase atomic system from a DFTK model and associated magnetic moments or from the usual lattice, atoms and positions list used in DFTK plus magnetic moments.

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Brillouin.KPaths.irrfbz_pathMethod

Extract the high-symmetry $k$-point path corresponding to the passed model using Brillouin. Uses the conventions described in the reference work by Cracknell, Davies, Miller, and Love (CDML). Of note, this has minor differences to the $k$-path reference (Y. Himuma et. al. Comput. Mater. Sci. 128, 140 (2017)) underlying the path-choices of Brillouin.jl, specifically for oA and mC Bravais types.

If the cell is a supercell of a smaller primitive cell, the standard $k$-path of the associated primitive cell is returned. So, the high-symmetry $k$ points are those of the primitive cell Brillouin zone, not those of the supercell Brillouin zone.

The dim argument allows to artificially truncate the dimension of the employed model, e.g. allowing to plot a 2D bandstructure of a 3D model (useful for example for plotting band structures of sheets with dim=2).

Due to lacking support in Spglib.jl for two-dimensional lattices it is (a) assumed that model.lattice is a conventional lattice and (b) required to pass the space group number using the sgnum keyword argument.

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DFTK.CROPFunction

CROP-accelerated root-finding iteration for f, starting from x0 and keeping a history of m steps. Optionally warming specifies the number of non-accelerated steps to perform for warming up the history.

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DFTK.G_vectorsMethod
G_vectors(basis::PlaneWaveBasis)
+G_vectors(basis::PlaneWaveBasis, kpt::Kpoint)

The list of wave vectors $G$ in reduced (integer) coordinates of a basis or a $k$-point kpt.

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DFTK.G_vectorsMethod
G_vectors([architecture=AbstractArchitecture], fft_size::Tuple)

The wave vectors G in reduced (integer) coordinates for a cubic basis set of given sizes.

source
DFTK.G_vectors_cartMethod
G_vectors_cart(basis::PlaneWaveBasis)
+G_vectors_cart(basis::PlaneWaveBasis, kpt::Kpoint)

The list of $G$ vectors of a given basis or kpt, in cartesian coordinates.

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DFTK.Gplusk_vectorsMethod
Gplusk_vectors(basis::PlaneWaveBasis, kpt::Kpoint)

The list of $G + k$ vectors, in reduced coordinates.

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DFTK.Gplusk_vectors_cartMethod
Gplusk_vectors_cart(basis::PlaneWaveBasis, kpt::Kpoint)

The list of $G + k$ vectors, in cartesian coordinates.

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DFTK.HybridMixingMethod

The model for the susceptibility is

\[\begin{aligned} χ_0(r, r') &= (-D_\text{loc}(r) δ(r, r') + D_\text{loc}(r) D_\text{loc}(r') / D) \\ &+ \sqrt{L(x)} \text{IFFT} \frac{C_0 G^2}{4π (1 - C_0 G^2 / k_{TF}^2)} \text{FFT} \sqrt{L(x)} -\end{aligned}\]

where $C_0 = 1 - ε_r$, $D_\text{loc}$ is the local density of states, $D$ is the density of states and the same convention for parameters are used as in DielectricMixing. Additionally there is the real-space localization function L(r). For details see Herbst, Levitt 2020 arXiv:2009.01665

Important kwargs passed on to χ0Mixing

  • RPA: Is the random-phase approximation used for the kernel (i.e. only Hartree kernel is used and not XC kernel)
  • verbose: Run the GMRES in verbose mode.
  • reltol: Relative tolerance for GMRES
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DFTK.IncreaseMixingTemperatureMethod

Increase the temperature used for computing the SCF preconditioners. Initially the temperature is increased by a factor, which is then smoothly lowered towards the temperature used within the model as the SCF converges. Once the density change is below above_ρdiff the mixing temperature is equal to the model temperature.

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DFTK.LdosMixingMethod

The model for the susceptibility is

\[\begin{aligned} +\end{aligned}\]

where $C_0 = 1 - ε_r$, $D_\text{loc}$ is the local density of states, $D$ is the density of states and the same convention for parameters are used as in DielectricMixing. Additionally there is the real-space localization function L(r). For details see Herbst, Levitt 2020 arXiv:2009.01665

Important kwargs passed on to χ0Mixing

  • RPA: Is the random-phase approximation used for the kernel (i.e. only Hartree kernel is used and not XC kernel)
  • verbose: Run the GMRES in verbose mode.
  • reltol: Relative tolerance for GMRES
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DFTK.IncreaseMixingTemperatureMethod

Increase the temperature used for computing the SCF preconditioners. Initially the temperature is increased by a factor, which is then smoothly lowered towards the temperature used within the model as the SCF converges. Once the density change is below above_ρdiff the mixing temperature is equal to the model temperature.

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DFTK.LdosMixingMethod

The model for the susceptibility is

\[\begin{aligned} χ_0(r, r') &= (-D_\text{loc}(r) δ(r, r') + D_\text{loc}(r) D_\text{loc}(r') / D) -\end{aligned}\]

where $D_\text{loc}$ is the local density of states, $D$ is the density of states. For details see Herbst, Levitt 2020 arXiv:2009.01665.

Important kwargs passed on to χ0Mixing

  • RPA: Is the random-phase approximation used for the kernel (i.e. only Hartree kernel is used and not XC kernel)
  • verbose: Run the GMRES in verbose mode.
  • reltol: Relative tolerance for GMRES
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DFTK.ScfAcceptImprovingStepMethod

Accept a step if the energy is at most increasing by max_energy and the residual is at most max_relative_residual times the residual in the previous step.

source
DFTK.ScfDiagtolMethod

Determine the tolerance used for the next diagonalization. This function takes $|ρnext - ρin|$ and multiplies it with ratio_ρdiff to get the next diagtol, ensuring additionally that the returned value is between diagtol_min and diagtol_max and never increases.

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DFTK.ScfPlotTraceFunction

Plot the trace of an SCF, i.e. the absolute error of the total energy at each iteration versus the converged energy in a semilog plot. By default a new plot canvas is generated, but an existing one can be passed and reused along with kwargs for the call to plot!. Requires Plots to be loaded.

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DFTK.apply_KMethod
apply_K(basis::PlaneWaveBasis, δψ, ψ, ρ, occupation)

Compute the application of K defined at ψ to δψ. ρ is the density issued from ψ. δψ also generates a δρ, computed with compute_δρ.

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DFTK.apply_kernelMethod
apply_kernel(basis::PlaneWaveBasis, δρ; kwargs...)

Computes the potential response to a perturbation δρ in real space, as a 4D (i,j,k,σ) array.

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DFTK.apply_symopMethod

Apply a symmetry operation to eigenvectors ψk at a given kpoint to obtain an equivalent point in [-0.5, 0.5)^3 and associated eigenvectors (expressed in the basis of the new $k$-point).

source
DFTK.apply_ΩMethod
apply_Ω(δψ, ψ, H::Hamiltonian, Λ)

Compute the application of Ω defined at ψ to δψ. H is the Hamiltonian computed from ψ and Λ is the set of Rayleigh coefficients ψk' * Hk * ψk at each k-point.

source
DFTK.apply_χ0Method

Get the density variation δρ corresponding to a potential variation δV.

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DFTK.attach_pspMethod
attach_psp(system::AbstractSystem, pspmap::AbstractDict{Symbol,String})
-attach_psp(system::AbstractSystem; psps::String...)

Return a new system with the pseudopotential property of all atoms set according to the passed pspmap, which maps from the atomic symbol to a pseudopotential identifier. Alternatively the mapping from atomic symbol to pseudopotential identifier can also be passed as keyword arguments. An empty string can be used to denote elements where the full Coulomb potential should be employed.

Examples

Select pseudopotentials for all silicon and oxygen atoms in the system.

julia> attach_psp(system, Dict(:Si => "hgh/lda/si-q4", :O => "hgh/lda/o-q6")

Same thing but using the kwargs syntax:

julia> attach_psp(system, Si="hgh/lda/si-q4", O="hgh/lda/o-q6")
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DFTK.build_fft_plans!Method

Plan a FFT of type T and size fft_size, spending some time on finding an optimal algorithm. (Inplace, out-of-place) x (forward, backward) FFT plans are returned.

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DFTK.build_projection_vectors_Method

Build projection vectors for a atoms array generated by term_nonlocal

\[\begin{aligned} +\end{aligned}\]

where $D_\text{loc}$ is the local density of states, $D$ is the density of states. For details see Herbst, Levitt 2020 arXiv:2009.01665.

Important kwargs passed on to χ0Mixing

  • RPA: Is the random-phase approximation used for the kernel (i.e. only Hartree kernel is used and not XC kernel)
  • verbose: Run the GMRES in verbose mode.
  • reltol: Relative tolerance for GMRES
source
DFTK.ScfAcceptImprovingStepMethod

Accept a step if the energy is at most increasing by max_energy and the residual is at most max_relative_residual times the residual in the previous step.

source
DFTK.ScfDiagtolMethod

Determine the tolerance used for the next diagonalization. This function takes $|ρnext - ρin|$ and multiplies it with ratio_ρdiff to get the next diagtol, ensuring additionally that the returned value is between diagtol_min and diagtol_max and never increases.

source
DFTK.ScfPlotTraceFunction

Plot the trace of an SCF, i.e. the absolute error of the total energy at each iteration versus the converged energy in a semilog plot. By default a new plot canvas is generated, but an existing one can be passed and reused along with kwargs for the call to plot!. Requires Plots to be loaded.

source
DFTK.apply_KMethod
apply_K(basis::PlaneWaveBasis, δψ, ψ, ρ, occupation)

Compute the application of K defined at ψ to δψ. ρ is the density issued from ψ. δψ also generates a δρ, computed with compute_δρ.

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DFTK.apply_kernelMethod
apply_kernel(basis::PlaneWaveBasis, δρ; kwargs...)

Computes the potential response to a perturbation δρ in real space, as a 4D (i,j,k,σ) array.

source
DFTK.apply_symopMethod

Apply a symmetry operation to eigenvectors ψk at a given kpoint to obtain an equivalent point in [-0.5, 0.5)^3 and associated eigenvectors (expressed in the basis of the new $k$-point).

source
DFTK.apply_ΩMethod
apply_Ω(δψ, ψ, H::Hamiltonian, Λ)

Compute the application of Ω defined at ψ to δψ. H is the Hamiltonian computed from ψ and Λ is the set of Rayleigh coefficients ψk' * Hk * ψk at each k-point.

source
DFTK.apply_χ0Method

Get the density variation δρ corresponding to a potential variation δV.

source
DFTK.attach_pspMethod
attach_psp(system::AbstractSystem, pspmap::AbstractDict{Symbol,String})
+attach_psp(system::AbstractSystem; psps::String...)

Return a new system with the pseudopotential property of all atoms set according to the passed pspmap, which maps from the atomic symbol to a pseudopotential identifier. Alternatively the mapping from atomic symbol to pseudopotential identifier can also be passed as keyword arguments. An empty string can be used to denote elements where the full Coulomb potential should be employed.

Examples

Select pseudopotentials for all silicon and oxygen atoms in the system.

julia> attach_psp(system, Dict(:Si => "hgh/lda/si-q4", :O => "hgh/lda/o-q6")

Same thing but using the kwargs syntax:

julia> attach_psp(system, Si="hgh/lda/si-q4", O="hgh/lda/o-q6")
source
DFTK.build_fft_plans!Method

Plan a FFT of type T and size fft_size, spending some time on finding an optimal algorithm. (Inplace, out-of-place) x (forward, backward) FFT plans are returned.

source
DFTK.build_projection_vectors_Method

Build projection vectors for a atoms array generated by term_nonlocal

\[\begin{aligned} H_{\rm at} &= \sum_{ij} C_{ij} \ket{p_i} \bra{p_j} \\ H_{\rm per} &= \sum_R \sum_{ij} C_{ij} \ket{p_i(x-R)} \bra{p_j(x-R)} \end{aligned}\]

\[\begin{aligned} \braket{e_k(G') \middle| H_{\rm per}}{e_k(G)} &= \ldots \\ &= \frac{1}{Ω} \sum_{ij} C_{ij} \hat p_i(k+G') \hat p_j^*(k+G), -\end{aligned}\]

where $\hat p_i(q) = ∫_{ℝ^3} p_i(r) e^{-iq·r} dr$.

We store $\frac{1}{\sqrt Ω} \hat p_i(k+G)$ in proj_vectors.

source
DFTK.bzmesh_ir_wedgeMethod
 bzmesh_ir_wedge(kgrid_size, symmetries; kshift=[0, 0, 0])

Construct the irreducible wedge of a uniform Brillouin zone mesh for sampling $k$-points, given the crystal symmetries symmetries. Returns the list of irreducible $k$-point (fractional) coordinates, the associated weights and the new symmetries compatible with the grid.

source
DFTK.bzmesh_uniformMethod
bzmesh_uniform(kgrid_size; kshift=[0, 0, 0])

Construct a (shifted) uniform Brillouin zone mesh for sampling the $k$-points. Returns all $k$-point coordinates, appropriate weights and the identity SymOp.

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DFTK.cell_to_supercellMethod

Transpose all data from a given self-consistent-field result from unit cell to supercell conventions. The parameters to adapt are the following:

  • basis_supercell and ψ_supercell are computed by the routines above.
  • The supercell occupations vector is the concatenation of all input occupations vectors.
  • The supercell density is computed with supercell occupations and ψ_supercell.
  • Supercell energies are the multiplication of input energies by the number of unit cells in the supercell.

Other parameters stay untouched.

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DFTK.cell_to_supercellMethod

Construct a plane-wave basis whose unit cell is the supercell associated to an input basis $k$-grid. All other parameters are modified so that the respective physical systems associated to both basis are equivalent.

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DFTK.cell_to_supercellMethod

Re-organize Bloch waves computed in a given basis as Bloch waves of the associated supercell basis. The output ψ_supercell have a single component at $Γ$-point, such that ψ_supercell[Γ][:, k+n] contains ψ[k][:, n], within normalization on the supercell.

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DFTK.cg!Method

Implementation of the conjugate gradient method which allows for preconditioning and projection operations along iterations.

source
DFTK.compute_Ak_gaussian_guessMethod

Compute the matrix $[A_k]_{m,n} = \langle ψ_m^k | g^{\text{per}}_n \rangle$

$g^{per}_n$ are periodized gaussians whose respective centers are given as an (num_bands,1) array [ [center 1], ... ].

Centers are to be given in lattice coordinates and G_vectors in reduced coordinates. The dot product is computed in the Fourier space.

Given an orbital $g_n$, the periodized orbital is defined by : $g^{per}_n = \sum\limits_{R \in {\rm lattice}} g_n( \cdot - R)$. The Fourier coefficient of $g^{per}_n$ at any G is given by the value of the Fourier transform of $g_n$ in G.

source
DFTK.compute_densityMethod
compute_density(basis::PlaneWaveBasis, ψ::AbstractVector, occupation::AbstractVector)

Compute the density for a wave function ψ discretized on the plane-wave grid basis, where the individual k-points are occupied according to occupation. ψ should be one coefficient matrix per $k$-point. It is possible to ask only for occupations higher than a certain level to be computed by using an optional occupation_threshold. By default all occupation numbers are considered.

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DFTK.compute_dynmatMethod

Compute the dynamical matrix in the form of a $3×n_{ m atoms}×3×n_{ m atoms}$ tensor in reduced coordinates.

source
DFTK.compute_fft_sizeMethod

Determine the minimal grid size for the cubic basis set to be able to represent product of orbitals (with the default supersampling=2).

Optionally optimize the grid afterwards for the FFT procedure by ensuring factorization into small primes.

The function will determine the smallest parallelepiped containing the wave vectors $|G|^2/2 \leq E_\text{cut} ⋅ \text{supersampling}^2$. For an exact representation of the density resulting from wave functions represented in the spherical basis sets, supersampling should be at least 2.

If factors is not empty, ensure that the resulting fft_size contains all the factors

source
DFTK.compute_forcesMethod

Compute the forces of an obtained SCF solution. Returns the forces wrt. the fractional lattice vectors. To get cartesian forces use compute_forces_cart. Returns a list of lists of forces (as SVector{3}) in the same order as the atoms and positions in the underlying Model.

source
DFTK.compute_forces_cartMethod

Compute the cartesian forces of an obtained SCF solution in Hartree / Bohr. Returns a list of lists of forces [[force for atom in positions] for (element, positions) in atoms] which has the same structure as the atoms object passed to the underlying Model.

source
DFTK.compute_kernelMethod
compute_kernel(basis::PlaneWaveBasis; kwargs...)

Computes a matrix representation of the full response kernel (derivative of potential with respect to density) in real space. For non-spin-polarized calculations the matrix dimension is prod(basis.fft_size) × prod(basis.fft_size) and for collinear spin-polarized cases it is 2prod(basis.fft_size) × 2prod(basis.fft_size). In this case the matrix has effectively 4 blocks

\[\left(\begin{array}{cc} +\end{aligned}\]

where $\hat p_i(q) = ∫_{ℝ^3} p_i(r) e^{-iq·r} dr$.

We store $\frac{1}{\sqrt Ω} \hat p_i(k+G)$ in proj_vectors.

source
DFTK.bzmesh_ir_wedgeMethod
 bzmesh_ir_wedge(kgrid_size, symmetries; kshift=[0, 0, 0])

Construct the irreducible wedge of a uniform Brillouin zone mesh for sampling $k$-points, given the crystal symmetries symmetries. Returns the list of irreducible $k$-point (fractional) coordinates, the associated weights and the new symmetries compatible with the grid.

source
DFTK.bzmesh_uniformMethod
bzmesh_uniform(kgrid_size; kshift=[0, 0, 0])

Construct a (shifted) uniform Brillouin zone mesh for sampling the $k$-points. Returns all $k$-point coordinates, appropriate weights and the identity SymOp.

source
DFTK.cell_to_supercellMethod

Transpose all data from a given self-consistent-field result from unit cell to supercell conventions. The parameters to adapt are the following:

  • basis_supercell and ψ_supercell are computed by the routines above.
  • The supercell occupations vector is the concatenation of all input occupations vectors.
  • The supercell density is computed with supercell occupations and ψ_supercell.
  • Supercell energies are the multiplication of input energies by the number of unit cells in the supercell.

Other parameters stay untouched.

source
DFTK.cell_to_supercellMethod

Construct a plane-wave basis whose unit cell is the supercell associated to an input basis $k$-grid. All other parameters are modified so that the respective physical systems associated to both basis are equivalent.

source
DFTK.cell_to_supercellMethod

Re-organize Bloch waves computed in a given basis as Bloch waves of the associated supercell basis. The output ψ_supercell have a single component at $Γ$-point, such that ψ_supercell[Γ][:, k+n] contains ψ[k][:, n], within normalization on the supercell.

source
DFTK.cg!Method

Implementation of the conjugate gradient method which allows for preconditioning and projection operations along iterations.

source
DFTK.compute_Ak_gaussian_guessMethod

Compute the matrix $[A_k]_{m,n} = \langle ψ_m^k | g^{\text{per}}_n \rangle$

$g^{per}_n$ are periodized gaussians whose respective centers are given as an (num_bands,1) array [ [center 1], ... ].

Centers are to be given in lattice coordinates and G_vectors in reduced coordinates. The dot product is computed in the Fourier space.

Given an orbital $g_n$, the periodized orbital is defined by : $g^{per}_n = \sum\limits_{R \in {\rm lattice}} g_n( \cdot - R)$. The Fourier coefficient of $g^{per}_n$ at any G is given by the value of the Fourier transform of $g_n$ in G.

source
DFTK.compute_densityMethod
compute_density(basis::PlaneWaveBasis, ψ::AbstractVector, occupation::AbstractVector)

Compute the density for a wave function ψ discretized on the plane-wave grid basis, where the individual k-points are occupied according to occupation. ψ should be one coefficient matrix per $k$-point. It is possible to ask only for occupations higher than a certain level to be computed by using an optional occupation_threshold. By default all occupation numbers are considered.

source
DFTK.compute_dynmatMethod

Compute the dynamical matrix in the form of a $3×n_{ m atoms}×3×n_{ m atoms}$ tensor in reduced coordinates.

source
DFTK.compute_fft_sizeMethod

Determine the minimal grid size for the cubic basis set to be able to represent product of orbitals (with the default supersampling=2).

Optionally optimize the grid afterwards for the FFT procedure by ensuring factorization into small primes.

The function will determine the smallest parallelepiped containing the wave vectors $|G|^2/2 \leq E_\text{cut} ⋅ \text{supersampling}^2$. For an exact representation of the density resulting from wave functions represented in the spherical basis sets, supersampling should be at least 2.

If factors is not empty, ensure that the resulting fft_size contains all the factors

source
DFTK.compute_forcesMethod

Compute the forces of an obtained SCF solution. Returns the forces wrt. the fractional lattice vectors. To get cartesian forces use compute_forces_cart. Returns a list of lists of forces (as SVector{3}) in the same order as the atoms and positions in the underlying Model.

source
DFTK.compute_forces_cartMethod

Compute the cartesian forces of an obtained SCF solution in Hartree / Bohr. Returns a list of lists of forces [[force for atom in positions] for (element, positions) in atoms] which has the same structure as the atoms object passed to the underlying Model.

source
DFTK.compute_kernelMethod
compute_kernel(basis::PlaneWaveBasis; kwargs...)

Computes a matrix representation of the full response kernel (derivative of potential with respect to density) in real space. For non-spin-polarized calculations the matrix dimension is prod(basis.fft_size) × prod(basis.fft_size) and for collinear spin-polarized cases it is 2prod(basis.fft_size) × 2prod(basis.fft_size). In this case the matrix has effectively 4 blocks

\[\left(\begin{array}{cc} K_{αα} & K_{αβ}\\ K_{βα} & K_{ββ} -\end{array}\right)\]

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DFTK.compute_ldosMethod

Local density of states, in real space. weight_threshold is a threshold to screen away small contributions to the LDOS.

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DFTK.compute_occupationMethod

Compute occupation and Fermi level given eigenvalues and using fermialg. The tol_n_elec gives the accuracy on the electron count which should be at least achieved.

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DFTK.compute_recip_latticeMethod

Compute the reciprocal lattice. We use the convention that the reciprocal lattice is the set of G vectors such that G ⋅ R ∈ 2π ℤ for all R in the lattice.

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DFTK.compute_transfer_matrixMethod

Return a list of sparse matrices (one per $k$-point) that map quantities given in the basis_in basis to quantities given in the basis_out basis.

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DFTK.compute_δocc!Method

Compute the derivatives of the occupations (and of the Fermi level). The derivatives of the occupations are in-place stored in δocc. The tuple (; δocc, δεF) is returned. It is assumed the passed δocc are initialised to zero.

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DFTK.compute_δψ!Method

Perform in-place computations of the derivatives of the wave functions by solving a Sternheimer equation for each k-points. It is assumed the passed δψ are initialised to zero.

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DFTK.compute_χ0Method

Compute the independent-particle susceptibility. Will blow up for large systems. For non-spin-polarized calculations the matrix dimension is prod(basis.fft_size) × prod(basis.fft_size) and for collinear spin-polarized cases it is 2prod(basis.fft_size) × 2prod(basis.fft_size). In this case the matrix has effectively 4 blocks, which are:

\[\left(\begin{array}{cc} +\end{array}\right)\]

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DFTK.compute_ldosMethod

Local density of states, in real space. weight_threshold is a threshold to screen away small contributions to the LDOS.

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DFTK.compute_occupationMethod

Compute occupation and Fermi level given eigenvalues and using fermialg. The tol_n_elec gives the accuracy on the electron count which should be at least achieved.

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DFTK.compute_recip_latticeMethod

Compute the reciprocal lattice. We use the convention that the reciprocal lattice is the set of G vectors such that G ⋅ R ∈ 2π ℤ for all R in the lattice.

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DFTK.compute_transfer_matrixMethod

Return a list of sparse matrices (one per $k$-point) that map quantities given in the basis_in basis to quantities given in the basis_out basis.

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DFTK.compute_δocc!Method

Compute the derivatives of the occupations (and of the Fermi level). The derivatives of the occupations are in-place stored in δocc. The tuple (; δocc, δεF) is returned. It is assumed the passed δocc are initialised to zero.

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DFTK.compute_δψ!Method

Perform in-place computations of the derivatives of the wave functions by solving a Sternheimer equation for each k-points. It is assumed the passed δψ are initialised to zero.

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DFTK.compute_χ0Method

Compute the independent-particle susceptibility. Will blow up for large systems. For non-spin-polarized calculations the matrix dimension is prod(basis.fft_size) × prod(basis.fft_size) and for collinear spin-polarized cases it is 2prod(basis.fft_size) × 2prod(basis.fft_size). In this case the matrix has effectively 4 blocks, which are:

\[\left(\begin{array}{cc} (χ_0)_{αα} & (χ_0)_{αβ} \\ (χ_0)_{βα} & (χ_0)_{ββ} -\end{array}\right)\]

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DFTK.count_n_projMethod
count_n_proj(psps, psp_positions)

Number of projector functions for all angular momenta up to psp.lmax and for all atoms in the system, including angular parts from -m:m.

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DFTK.count_n_projMethod
count_n_proj(psp, l)

Number of projector functions for angular momentum l, including angular parts from -m:m.

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DFTK.count_n_projMethod
count_n_proj(psp)

Number of projector functions for all angular momenta up to psp.lmax, including angular parts from -m:m.

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DFTK.create_supercellMethod

Construct a supercell of size supercell_size from a unit cell described by its lattice, atoms and their positions.

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DFTK.diagonalize_all_kblocksMethod

Function for diagonalising each $k$-Point blow of ham one step at a time. Some logic for interpolating between $k$-points is used if interpolate_kpoints is true and if no guesses are given. eigensolver is the iterative eigensolver that really does the work, operating on a single $k$-Block. eigensolver should support the API eigensolver(A, X0; prec, tol, maxiter) prec_type should be a function that returns a preconditioner when called as prec(ham, kpt)

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DFTK.direct_minimizationMethod

Computes the ground state by direct minimization. kwargs... are passed to Optim.Options(). Note that the resulting ψ are not necessarily eigenvectors of the Hamiltonian.

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DFTK.disable_threadingMethod

Convenience function to disable all threading in DFTK and assert that Julia threading is off as well.

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DFTK.divergence_realMethod

Compute divergence of an operand function, which returns the cartesian x,y,z components in real space when called with the arguments 1 to 3. The divergence is also returned as a real-space array.

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DFTK.energy_forces_ewaldMethod

Compute the electrostatic energy and forces. The energy is the electrostatic interaction energy per unit cell between point charges in a uniform background of compensating charge to yield net neutrality. The forces is the opposite of the derivative of the energy with respect to positions.

lattice should contain the lattice vectors as columns. charges and positions are the point charges and their positions (as an array of arrays) in fractional coordinates.

For now this function returns zero energy and force on non-3D systems. Use a pairwise potential term if you want to customise this treatment.

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DFTK.energy_forces_pairwiseMethod

Compute the pairwise energy and forces. The energy is the interaction energy per unit cell between atomic sites. The forces is the opposite of the derivative of the energy with respect to positions.

lattice should contain the lattice vectors as columns. symbols and positions are the atomic elements and their positions (as an array of arrays) in fractional coordinates. V and params are the pairwise potential and its set of parameters (that depends on pairs of symbols).

The potential is expected to decrease quickly at infinity.

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DFTK.energy_psp_correctionMethod

Compute the correction term for properly modelling the interaction of the pseudopotential core with the compensating background charge induced by the Ewald term.

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DFTK.enforce_real!Method

Ensure its real-space equivalent of passed Fourier-space representation is entirely real by removing wavevectors G that don't have a -G counterpart in the basis.

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DFTK.estimate_integer_lattice_boundsMethod

Estimate integer bounds for dense space loops from a given inequality ||Mx|| ≤ δ. For 1D and 2D systems the limit will be zero in the auxiliary dimensions.

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DFTK.eval_psp_density_core_fourierMethod
eval_psp_density_core_fourier(psp, q)

Evaluate the atomic core charge density in reciprocal space: ρval(q) = ∫{R^3} ρcore(r) e^{-iqr} dr = 4π ∫{R+} ρcore(r) sin(qr)/qr r^2 dr

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DFTK.eval_psp_density_valence_fourierMethod
eval_psp_density_valence_fourier(psp, q)

Evaluate the atomic valence charge density in reciprocal space: ρval(q) = ∫{R^3} ρval(r) e^{-iqr} dr = 4π ∫{R+} ρval(r) sin(qr)/qr r^2 dr

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DFTK.eval_psp_energy_correctionFunction
eval_psp_energy_correction([T=Float64,] psp, n_electrons)

Evaluate the energy correction to the Ewald electrostatic interaction energy of one unit cell, which is required compared the Ewald expression for point-like nuclei. n_electrons is the number of electrons per unit cell. This defines the uniform compensating background charge, which is assumed here.

Notice: The returned result is the energy per unit cell and not the energy per volume. To obtain the latter, the caller needs to divide by the unit cell volume.

The energy correction is defined as the limit of the Fourier-transform of the local potential as $q \to 0$, using the same correction as in the Fourier-transform of the local potential: math \lim_{q \to 0} 4π N_{\rm elec} ∫_{ℝ_+} (V(r) - C(r)) \frac{\sin(qr)}{qr} r^2 dr + F[C(r)] = 4π N_{\rm elec} ∫_{ℝ_+} (V(r) + Z/r) r^2 dr

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DFTK.eval_psp_local_fourierMethod
eval_psp_local_fourier(psp, q)

Evaluate the local part of the pseudopotential in reciprocal space:

\[\begin{aligned} +\end{array}\right)\]

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DFTK.count_n_projMethod
count_n_proj(psps, psp_positions)

Number of projector functions for all angular momenta up to psp.lmax and for all atoms in the system, including angular parts from -m:m.

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DFTK.count_n_projMethod
count_n_proj(psp, l)

Number of projector functions for angular momentum l, including angular parts from -m:m.

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DFTK.count_n_projMethod
count_n_proj(psp)

Number of projector functions for all angular momenta up to psp.lmax, including angular parts from -m:m.

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DFTK.create_supercellMethod

Construct a supercell of size supercell_size from a unit cell described by its lattice, atoms and their positions.

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DFTK.diagonalize_all_kblocksMethod

Function for diagonalising each $k$-Point blow of ham one step at a time. Some logic for interpolating between $k$-points is used if interpolate_kpoints is true and if no guesses are given. eigensolver is the iterative eigensolver that really does the work, operating on a single $k$-Block. eigensolver should support the API eigensolver(A, X0; prec, tol, maxiter) prec_type should be a function that returns a preconditioner when called as prec(ham, kpt)

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DFTK.direct_minimizationMethod

Computes the ground state by direct minimization. kwargs... are passed to Optim.Options(). Note that the resulting ψ are not necessarily eigenvectors of the Hamiltonian.

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DFTK.disable_threadingMethod

Convenience function to disable all threading in DFTK and assert that Julia threading is off as well.

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DFTK.divergence_realMethod

Compute divergence of an operand function, which returns the cartesian x,y,z components in real space when called with the arguments 1 to 3. The divergence is also returned as a real-space array.

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DFTK.energy_forces_ewaldMethod

Compute the electrostatic energy and forces. The energy is the electrostatic interaction energy per unit cell between point charges in a uniform background of compensating charge to yield net neutrality. The forces is the opposite of the derivative of the energy with respect to positions.

lattice should contain the lattice vectors as columns. charges and positions are the point charges and their positions (as an array of arrays) in fractional coordinates.

For now this function returns zero energy and force on non-3D systems. Use a pairwise potential term if you want to customise this treatment.

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DFTK.energy_forces_pairwiseMethod

Compute the pairwise energy and forces. The energy is the interaction energy per unit cell between atomic sites. The forces is the opposite of the derivative of the energy with respect to positions.

lattice should contain the lattice vectors as columns. symbols and positions are the atomic elements and their positions (as an array of arrays) in fractional coordinates. V and params are the pairwise potential and its set of parameters (that depends on pairs of symbols).

The potential is expected to decrease quickly at infinity.

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DFTK.energy_psp_correctionMethod

Compute the correction term for properly modelling the interaction of the pseudopotential core with the compensating background charge induced by the Ewald term.

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DFTK.enforce_real!Method

Ensure its real-space equivalent of passed Fourier-space representation is entirely real by removing wavevectors G that don't have a -G counterpart in the basis.

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DFTK.estimate_integer_lattice_boundsMethod

Estimate integer bounds for dense space loops from a given inequality ||Mx|| ≤ δ. For 1D and 2D systems the limit will be zero in the auxiliary dimensions.

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DFTK.eval_psp_density_core_fourierMethod
eval_psp_density_core_fourier(psp, q)

Evaluate the atomic core charge density in reciprocal space: ρval(q) = ∫{R^3} ρcore(r) e^{-iqr} dr = 4π ∫{R+} ρcore(r) sin(qr)/qr r^2 dr

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DFTK.eval_psp_density_valence_fourierMethod
eval_psp_density_valence_fourier(psp, q)

Evaluate the atomic valence charge density in reciprocal space: ρval(q) = ∫{R^3} ρval(r) e^{-iqr} dr = 4π ∫{R+} ρval(r) sin(qr)/qr r^2 dr

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DFTK.eval_psp_energy_correctionFunction
eval_psp_energy_correction([T=Float64,] psp, n_electrons)

Evaluate the energy correction to the Ewald electrostatic interaction energy of one unit cell, which is required compared the Ewald expression for point-like nuclei. n_electrons is the number of electrons per unit cell. This defines the uniform compensating background charge, which is assumed here.

Notice: The returned result is the energy per unit cell and not the energy per volume. To obtain the latter, the caller needs to divide by the unit cell volume.

The energy correction is defined as the limit of the Fourier-transform of the local potential as $q \to 0$, using the same correction as in the Fourier-transform of the local potential: math \lim_{q \to 0} 4π N_{\rm elec} ∫_{ℝ_+} (V(r) - C(r)) \frac{\sin(qr)}{qr} r^2 dr + F[C(r)] = 4π N_{\rm elec} ∫_{ℝ_+} (V(r) + Z/r) r^2 dr

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DFTK.eval_psp_local_fourierMethod
eval_psp_local_fourier(psp, q)

Evaluate the local part of the pseudopotential in reciprocal space:

\[\begin{aligned} V_{\rm loc}(q) &= ∫_{ℝ^3} V_{\rm loc}(r) e^{-iqr} dr \\ &= 4π ∫_{ℝ_+} V_{\rm loc}(r) \frac{\sin(qr)}{q} r dr \end{aligned}\]

In practice, the local potential should be corrected using a Coulomb-like term $C(r) = -Z/r$ to remove the long-range tail of $V_{\rm loc}(r)$ from the integral:

\[\begin{aligned} V_{\rm loc}(q) &= ∫_{ℝ^3} (V_{\rm loc}(r) - C(r)) e^{-iq·r} dr + F[C(r)] \\ &= 4π ∫_{ℝ_+} (V_{\rm loc}(r) + Z/r) \frac{\sin(qr)}{qr} r^2 dr - Z/q^2 -\end{aligned}\]

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DFTK.eval_psp_projector_fourierMethod
eval_psp_projector_fourier(psp, i, l, q)

Evaluate the radial part of the i-th projector for angular momentum l at the reciprocal vector with modulus q:

\[\begin{aligned} +\end{aligned}\]

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DFTK.eval_psp_projector_fourierMethod
eval_psp_projector_fourier(psp, i, l, q)

Evaluate the radial part of the i-th projector for angular momentum l at the reciprocal vector with modulus q:

\[\begin{aligned} p(q) &= ∫_{ℝ^3} p_{il}(r) e^{-iq·r} dr \\ &= 4π ∫_{ℝ_+} r^2 p_{il}(r) j_l(qr) dr -\end{aligned}\]

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DFTK.eval_psp_projector_realMethod
eval_psp_projector_real(psp, i, l, r)

Evaluate the radial part of the i-th projector for angular momentum l in real-space at the vector with modulus r.

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DFTK.find_equivalent_kptMethod

Find the equivalent index of the coordinate kcoord ∈ ℝ³ in a list kcoords ∈ [-½, ½)³. ΔG is the vector of ℤ³ such that kcoords[index] = kcoord + ΔG.

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DFTK.gather_kptsMethod

Gather the distributed data of a quantity depending on k-Points on the master process and return it. On the other (non-master) processes nothing is returned.

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DFTK.gather_kptsMethod

Gather the distributed $k$-point data on the master process and return it as a PlaneWaveBasis. On the other (non-master) processes nothing is returned. The returned object should not be used for computations and only to extract data for post-processing and serialisation to disk.

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DFTK.guess_densityFunction
guess_density(basis::PlaneWaveBasis, method::DensityConstructionMethod,
-              magnetic_moments=[]; n_electrons=basis.model.n_electrons)

Build a superposition of atomic densities (SAD) guess density or a rarndom guess density.

The guess atomic densities are taken as one of the following depending on the input method:

-RandomDensity(): A random density, normalized to the number of electrons basis.model.n_electrons. Does not support magnetic moments. -ValenceDensityAuto(): A combination of the ValenceDensityGaussian and ValenceDensityPseudo methods where elements whose pseudopotentials provide numeric valence charge density data use them and elements without use Gaussians. -ValenceDensityGaussian(): Gaussians of length specified by atom_decay_length normalized for the correct number of electrons:

\[\hat{ρ}(G) = Z_{\mathrm{valence}} \exp\left(-(2π \text{length} |G|)^2\right)\]

  • ValenceDensityPseudo(): Numerical pseudo-atomic valence charge densities from the

pseudopotentials. Will fail if one or more elements in the system has a pseudopotential that does not have valence charge density data.

When magnetic moments are provided, construct a symmetry-broken density guess. The magnetic moments should be specified in units of $μ_B$.

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DFTK.index_G_vectorsMethod

Return the index tuple I such that G_vectors(basis)[I] == G or the index i such that G_vectors(basis, kpoint)[i] == G. Returns nothing if outside the range of valid wave vectors.

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DFTK.interpolate_densityMethod

Interpolate a function expressed in a basis basis_in to a basis basis_out. This interpolation uses a very basic real-space algorithm, and makes a DWIM-y attempt to take into account the fact that basis_out can be a supercell of basis_in.

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DFTK.interpolate_kpointMethod

Interpolate some data from one $k$-point to another. The interpolation is fast, but not necessarily exact. Intended only to construct guesses for iterative solvers.

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DFTK.irfftMethod

Perform a real valued iFFT; see ifft. Note that this function silently drops the imaginary part.

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DFTK.is_metalMethod
is_metal(eigenvalues, εF; tol)

Determine whether the provided bands indicate the material is a metal, i.e. where bands are cut by the Fermi level.

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DFTK.k_to_kpq_mappingMethod

Return the indices of the kpoints shifted by q. That is for each kpoint of the basis: kpoints[ik].coordinate + q = kpoints[indices[ik]].coordinate.

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DFTK.kgrid_from_minimal_n_kpointsMethod

Selects a kgrid size which ensures that at least a n_kpoints total number of $k$-points are used. The distribution of $k$-points amongst coordinate directions is as uniformly as possible, trying to achieve an identical minimal spacing in all directions.

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DFTK.kgrid_from_minimal_spacingMethod

Selects a kgrid size to ensure a minimal spacing (in inverse Bohrs) between kpoints. A reasonable spacing is 0.13 inverse Bohrs (around $2π * 0.04 \AA^{-1}$).

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DFTK.list_pspFunction
list_psp(element; functional, family, core)

List the pseudopotential files known to DFTK. Allows various ways to restrict the displayed files.

Examples

julia> list_psp(family="hgh")

will list all HGH-type pseudopotentials and

julia> list_psp(family="hgh", functional="lda")

will only list those for LDA (also known as Pade in this context) and

julia> list_psp(:O, core=:semicore)

will list all oxygen semicore pseudopotentials known to DFTK.

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DFTK.load_pspMethod

Load a pseudopotential file from the library of pseudopotentials. The file is searched in the directory datadir_psp() and by the key. If the key is a path to a valid file, the extension is used to determine the type of the pseudopotential file format and a respective class is returned.

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DFTK.model_DFTMethod

Build a DFT model from the specified atoms, with the specified functionals.

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DFTK.model_LDAMethod

Build an LDA model (Perdew & Wang parametrization) from the specified atoms. DOI:10.1103/PhysRevB.45.13244

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DFTK.model_PBEMethod

Build an PBE-GGA model from the specified atoms. DOI:10.1103/PhysRevLett.77.3865

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DFTK.model_SCANMethod

Build a SCAN meta-GGA model from the specified atoms. DOI:10.1103/PhysRevLett.115.036402

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DFTK.model_atomicMethod

Convenience constructor, which builds a standard atomic (kinetic + atomic potential) model. Use extra_terms to add additional terms.

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DFTK.mpi_nprocsFunction

Number of processors used in MPI. Can be called without ensuring initialization.

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DFTK.multiply_by_expiqrMethod

Return the Fourier coefficients for ψk · e^{i q·r} in the basis of kpt_out, where ψk is defined on a basis kpt_in.

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DFTK.eval_psp_projector_realMethod
eval_psp_projector_real(psp, i, l, r)

Evaluate the radial part of the i-th projector for angular momentum l in real-space at the vector with modulus r.

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DFTK.find_equivalent_kptMethod

Find the equivalent index of the coordinate kcoord ∈ ℝ³ in a list kcoords ∈ [-½, ½)³. ΔG is the vector of ℤ³ such that kcoords[index] = kcoord + ΔG.

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DFTK.gather_kptsMethod

Gather the distributed data of a quantity depending on k-Points on the master process and return it. On the other (non-master) processes nothing is returned.

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DFTK.gather_kptsMethod

Gather the distributed $k$-point data on the master process and return it as a PlaneWaveBasis. On the other (non-master) processes nothing is returned. The returned object should not be used for computations and only to extract data for post-processing and serialisation to disk.

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DFTK.guess_densityFunction
guess_density(basis::PlaneWaveBasis, method::DensityConstructionMethod,
+              magnetic_moments=[]; n_electrons=basis.model.n_electrons)

Build a superposition of atomic densities (SAD) guess density or a rarndom guess density.

The guess atomic densities are taken as one of the following depending on the input method:

-RandomDensity(): A random density, normalized to the number of electrons basis.model.n_electrons. Does not support magnetic moments. -ValenceDensityAuto(): A combination of the ValenceDensityGaussian and ValenceDensityPseudo methods where elements whose pseudopotentials provide numeric valence charge density data use them and elements without use Gaussians. -ValenceDensityGaussian(): Gaussians of length specified by atom_decay_length normalized for the correct number of electrons:

\[\hat{ρ}(G) = Z_{\mathrm{valence}} \exp\left(-(2π \text{length} |G|)^2\right)\]

  • ValenceDensityPseudo(): Numerical pseudo-atomic valence charge densities from the

pseudopotentials. Will fail if one or more elements in the system has a pseudopotential that does not have valence charge density data.

When magnetic moments are provided, construct a symmetry-broken density guess. The magnetic moments should be specified in units of $μ_B$.

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DFTK.index_G_vectorsMethod

Return the index tuple I such that G_vectors(basis)[I] == G or the index i such that G_vectors(basis, kpoint)[i] == G. Returns nothing if outside the range of valid wave vectors.

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DFTK.interpolate_densityMethod

Interpolate a function expressed in a basis basis_in to a basis basis_out. This interpolation uses a very basic real-space algorithm, and makes a DWIM-y attempt to take into account the fact that basis_out can be a supercell of basis_in.

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DFTK.interpolate_kpointMethod

Interpolate some data from one $k$-point to another. The interpolation is fast, but not necessarily exact. Intended only to construct guesses for iterative solvers.

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DFTK.irfftMethod

Perform a real valued iFFT; see ifft. Note that this function silently drops the imaginary part.

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DFTK.is_metalMethod
is_metal(eigenvalues, εF; tol)

Determine whether the provided bands indicate the material is a metal, i.e. where bands are cut by the Fermi level.

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DFTK.k_to_kpq_mappingMethod

Return the indices of the kpoints shifted by q. That is for each kpoint of the basis: kpoints[ik].coordinate + q = kpoints[indices[ik]].coordinate.

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DFTK.kgrid_from_minimal_n_kpointsMethod

Selects a kgrid size which ensures that at least a n_kpoints total number of $k$-points are used. The distribution of $k$-points amongst coordinate directions is as uniformly as possible, trying to achieve an identical minimal spacing in all directions.

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DFTK.kgrid_from_minimal_spacingMethod

Selects a kgrid size to ensure a minimal spacing (in inverse Bohrs) between kpoints. A reasonable spacing is 0.13 inverse Bohrs (around $2π * 0.04 \AA^{-1}$).

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DFTK.list_pspFunction
list_psp(element; functional, family, core)

List the pseudopotential files known to DFTK. Allows various ways to restrict the displayed files.

Examples

julia> list_psp(family="hgh")

will list all HGH-type pseudopotentials and

julia> list_psp(family="hgh", functional="lda")

will only list those for LDA (also known as Pade in this context) and

julia> list_psp(:O, core=:semicore)

will list all oxygen semicore pseudopotentials known to DFTK.

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DFTK.load_pspMethod

Load a pseudopotential file from the library of pseudopotentials. The file is searched in the directory datadir_psp() and by the key. If the key is a path to a valid file, the extension is used to determine the type of the pseudopotential file format and a respective class is returned.

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DFTK.model_DFTMethod

Build a DFT model from the specified atoms, with the specified functionals.

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DFTK.model_LDAMethod

Build an LDA model (Perdew & Wang parametrization) from the specified atoms. DOI:10.1103/PhysRevB.45.13244

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DFTK.model_PBEMethod

Build an PBE-GGA model from the specified atoms. DOI:10.1103/PhysRevLett.77.3865

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DFTK.model_SCANMethod

Build a SCAN meta-GGA model from the specified atoms. DOI:10.1103/PhysRevLett.115.036402

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DFTK.model_atomicMethod

Convenience constructor, which builds a standard atomic (kinetic + atomic potential) model. Use extra_terms to add additional terms.

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DFTK.mpi_nprocsFunction

Number of processors used in MPI. Can be called without ensuring initialization.

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DFTK.multiply_by_expiqrMethod

Return the Fourier coefficients for ψk · e^{i q·r} in the basis of kpt_out, where ψk is defined on a basis kpt_in.

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DFTK.newtonMethod
newton(basis::PlaneWaveBasis{T}, ψ0;
        tol=1e-6, tol_cg=tol / 100, maxiter=20, callback=ScfDefaultCallback(),
-       is_converged=ScfConvergenceDensity(tol))

Newton algorithm. Be careful that the starting point needs to be not too far from the solution.

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DFTK.next_compatible_fft_sizeMethod

Find the next compatible FFT size Sizes must (a) be a product of small primes only and (b) contain the factors. If smallprimes is empty (a) is skipped.

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DFTK.next_densityFunction

Obtain new density ρ by diagonalizing ham. Follows the policy imposed by the bands data structure to determine and adjust the number of bands to be computed.

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DFTK.norm_cplxMethod

Complex-analytic extension of LinearAlgebra.norm(x) from real to complex inputs. Needed for phonons as we want to perform a matrix-vector product f'(x)·h, where f is a real-to-real function and h a complex vector. To do this using automatic differentiation, we can extend analytically f to accept complex inputs, then differentiate t -> f(x+t·h). This will fail if non-analytic functions like norm are used for complex inputs, and therefore we have to redefine it.

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DFTK.overlap_Mmn_k_kpbMethod

Computes the matrix $[M^{k,b}]_{m,n} = \langle u_{m,k} | u_{n,k+b} \rangle$ for given k, kpb = $k+b$.

G_shift is the "shifting" vector, correction due to the periodicity conditions imposed on $k \to ψ_k$. It is non zero if kpb is taken in another unit cell of the reciprocal lattice. We use here that: $u_{n(k + G_{\rm shift})}(r) = e^{-i*\langle G_{\rm shift},r \rangle} u_{nk}$.

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DFTK.plot_bandstructureFunction

Compute and plot the band structure. n_bands selects the number of bands to compute. If this value is absent and an scfres is used to start the calculation a default of n_bands_scf + 5sqrt(n_bands_scf) is used. The unit used to plot the bands can be selected using the unit parameter. Like in the rest of DFTK Hartree is used by default. Another standard choices is unit=u"eV" (electron volts). The kline_density is given in number of $k$-points per inverse bohrs (i.e. overall in units of length).

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DFTK.plot_dosFunction

Plot the density of states over a reasonable range. Requires to load Plots.jl beforehand.

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DFTK.psp_local_polynomialFunction

The local potential of a HGH pseudopotentials in reciprocal space can be brought to the form $Q(t) / (t^2 exp(t^2 / 2))$ where $t = r_\text{loc} q$ and Q is a polynomial of at most degree 8. This function returns Q.

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DFTK.psp_projector_polynomialFunction

The nonlocal projectors of a HGH pseudopotentials in reciprocal space can be brought to the form $Q(t) exp(-t^2 / 2)$ where $t = r_l q$ and Q is a polynomial. This function returns Q.

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DFTK.qcut_psp_localMethod

Estimate an upper bound for the argument q after which abs(eval_psp_local_fourier(psp, q)) is a strictly decreasing function.

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DFTK.qcut_psp_projectorMethod

Estimate an upper bound for the argument q after which eval_psp_projector_fourier(psp, q) is a strictly decreasing function.

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DFTK.r_vectorsMethod
r_vectors(basis::PlaneWaveBasis)

The list of $r$ vectors, in reduced coordinates. By convention, this is in [0,1)^3.

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DFTK.read_w90_nnkpMethod

Read the .nnkp file provided by the preprocessing routine of Wannier90 (i.e. "wannier90.x -pp prefix") Returns:

  1. the array 'nnkpts' of k points, their respective nearest neighbors and associated shifing vectors (non zero if the neighbor is located in another cell).
  2. the number 'nntot' of neighbors per k point.

TODO: add the possibility to exclude bands

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DFTK.run_wannier90Function

Wannerize the obtained bands using wannier90. By default all converged bands from the scfres are employed (change with n_bands kwargs) and n_wannier = n_bands wannier functions are computed using random Gaussians as guesses. All keyword arguments supported by Wannier90 for the disentanglement may be added as keyword arguments. The function returns the fileprefix.

Experimental feature

Currently this is an experimental feature, which has not yet been tested to full depth. The interface is considered unstable and may change incompatibly in the future. Use at your own risk and please report bugs in case you encounter any.

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DFTK.save_scfresMethod
save_scfres(filename, scfres)

Save an scfres obtained from self_consistent_field to a file. The format is determined from the file extension. Currently the following file extensions are recognized and supported:

  • jld2: A JLD2 file. Stores the complete state and can be used (with load_scfres) to restart an SCF from a checkpoint or post-process an SCF solution. See Saving SCF results on disk and SCF checkpoints for details.
  • vts: A VTK file for visualisation e.g. in paraview. Stores the density, spin density and some metadata (energy, Fermi level, occupation etc.). Supports these keyword arguments:
    • save_ψ: Save the real-space representation of the orbitals as well (may lead to larger files).
    • extra_data: Dict{String,Array} with additional data on the 3D real-space grid to store into the VTK file.
  • json: A JSON file with basic information about the SCF run. Stores for example the number of iterations, occupations, norm of the most recent density change, eigenvalues, Fermi level etc.
No compatibility guarantees

No guarantees are made with respect to this function at this point. It may change incompatibly between DFTK versions or stop working / be removed in the future.

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DFTK.scf_damping_quadratic_modelMethod

Use the two iteration states info and info_next to find a damping value from a quadratic model for the SCF energy. Returns nothing if the constructed model is not considered trustworthy, else returns the suggested damping.

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DFTK.self_consistent_fieldMethod
self_consistent_field(basis; [tol, mixing, damping, ρ, ψ])

Solve the Kohn-Sham equations with a density-based SCF algorithm using damped, preconditioned iterations where $ρ_\text{next} = α P^{-1} (ρ_\text{out} - ρ_\text{in})$.

Overview of parameters:

  • ρ: Initial density
  • ψ: Initial orbitals
  • tol: Tolerance for the density change ($\|ρ_\text{out} - ρ_\text{in}\|$) to flag convergence. Default is 1e-6.
  • is_converged: Convergence control callback. Typical objects passed here are DFTK.ScfConvergenceDensity(tol) (the default), DFTK.ScfConvergenceEnergy(tol) or DFTK.ScfConvergenceForce(tol).
  • maxiter: Maximal number of SCF iterations
  • mixing: Mixing method, which determines the preconditioner $P^{-1}$ in the above equation. Typical mixings are LdosMixing, KerkerMixing, SimpleMixing or DielectricMixing. Default is LdosMixing()
  • damping: Damping parameter $α$ in the above equation. Default is 0.8.
  • nbandsalg: By default DFTK uses nbandsalg=AdaptiveBands(model), which adaptively determines the number of bands to compute. If you want to influence this algorithm or use a predefined number of bands in each SCF step, pass a FixedBands or AdaptiveBands. Beware that with non-zero temperature, the convergence of the SCF algorithm may be limited by the default_occupation_threshold parameter. For highly accurate calculations we thus recommend increasing the default_occupation_threshold of the AdaptiveBands.
  • callback: Function called at each SCF iteration. Usually takes care of printing the intermediate state.
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DFTK.solve_ΩplusKMethod
solve_ΩplusK(basis::PlaneWaveBasis{T}, ψ, res, occupation;
-             tol=1e-10, verbose=false) where {T}

Return δψ where (Ω+K) δψ = rhs

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DFTK.solve_ΩplusK_splitMethod

Solve the problem (Ω+K) δψ = rhs using a split algorithm, where rhs is typically -δHextψ (the negative matvec of an external perturbation with the SCF orbitals ψ) and δψ is the corresponding total variation in the orbitals ψ. Additionally returns: - δρ: Total variation in density) - δHψ: Total variation in Hamiltonian applied to orbitals - δeigenvalues: Total variation in eigenvalues - δVind: Change in potential induced by δρ (the term needed on top of δHextψ to get δHψ).

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DFTK.spglib_standardize_cellMethod

Returns crystallographic conventional cell according to the International Table of Crystallography Vol A (ITA) in case primitive=false. If primitive=true the primitive lattice is returned in the convention of the reference work of Cracknell, Davies, Miller, and Love (CDML). Of note this has minor differences to the primitive setting choice made in the ITA.

source
DFTK.sphericalbesselj_fastMethod
sphericalbesselj_fast(l::Integer, x::Number)

Returns the spherical Bessel function of the first kind jl(x). Consistent with https://en.wikipedia.org/wiki/Besselfunction#SphericalBesselfunctions and with SpecialFunctions.sphericalbesselj. Specialized for integer 0 <= l <= 5.

source
DFTK.standardize_atomsFunction

Apply various standardisations to a lattice and a list of atoms. It uses spglib to detect symmetries (within tol_symmetry), then cleans up the lattice according to the symmetries (unless correct_symmetry is false) and returns the resulting standard lattice and atoms. If primitive is true (default) the primitive unit cell is returned, else the conventional unit cell is returned.

source
DFTK.symmetrize_ρMethod

Symmetrize a density by applying all the basis (by default) symmetries and forming the average.

source
DFTK.symmetry_operationsFunction

Return the symmetries given an atomic structure with optionally designated magnetic moments on each of the atoms. The symmetries are determined using spglib.

source
DFTK.symmetry_operationsMethod

Return the Symmetry operations given a hall_number.

This function allows to directly access to the space group operations in the spglib database. To specify the space group type with a specific choice, hall_number is used.

The definition of hall_number is found at Space group type.

source
DFTK.synchronize_deviceMethod

Synchronize data and finish all operations on the execution stream of the device. This needs to be called explicitly before a task finishes (e.g. in an @spawn block).

source
DFTK.to_cpuMethod

Transfer an array from a device (typically a GPU) to the CPU.

source
DFTK.todictMethod

Convert an Energies struct to a dictionary representation

source
DFTK.transfer_blochwave_kptMethod

Transfer an array ψk_in expanded on kpt_in, and produce $ψ(r) e^{i ΔG·r}$ expanded on kpt_out. It is mostly useful for phonons. Beware: ψk_out can lose information if the shift ΔG is large or if the G_vectors differ between k-points.

source
DFTK.transfer_densityMethod

Transfer density (in real space) between two basis sets.

This function is fast by transferring only the Fourier coefficients from the small basis to the big basis.

Note that this implies that for even-sized small FFT grids doing the transfer small -> big -> small is not an identity (as the small basis has an unmatched Fourier component and the identity $c_G = c_{-G}^\ast$ does not fully hold).

Note further that for the direction big -> small employing this function does not give the same answer as using first transfer_blochwave and then compute_density.

source
DFTK.transfer_mappingMethod

Compute the index mapping between the global grids of two bases. Returns an iterator of 8 pairs (block_in, block_out). Iterated over these pairs x_out_fourier[block_out, :] = x_in_fourier[block_in, :] does the transfer from the Fourier coefficients x_in_fourier (defined on basis_in) to x_out_fourier (defined on basis_out, equally provided as Fourier coefficients).

source
DFTK.transfer_mappingMethod

Compute the index mapping between two bases. Returns two arrays idcs_in and idcs_out such that ψkout[idcs_out] = ψkin[idcs_in] does the transfer from ψkin (defined on basis_in and kpt_in) to ψkout (defined on basis_out and kpt_out).

source
DFTK.unfold_bzMethod

" Convert a basis into one that doesn't use BZ symmetry. This is mainly useful for debug purposes (e.g. in cases we don't want to bother thinking about symmetries).

source
DFTK.versioninfoFunction
DFTK.versioninfo([io::IO=stdout])

Summary of version and configuration of DFTK and its key dependencies.

source
DFTK.write_w90_winMethod

Write a win file at the indicated prefix. Parameters to Wannier90 can be added as kwargs: e.g. num_iter=500.

source
DFTK.ylm_realMethod

Returns the (l,m) real spherical harmonic Ylm(r). Consistent with https://en.wikipedia.org/wiki/Tableofsphericalharmonics#Realsphericalharmonics

source
DFTK.zeros_likeFunction

Create an array of same "array type" as X filled with zeros, minimizing the number of allocations. This unifies CPU and GPU code, as the output will always be on the same device as the input.

source
DFTK.@timingMacro

Shortened version of the @timeit macro from TimerOutputs, which writes to the DFTK timer.

source
DFTK.Smearing.entropyMethod

Entropy. Note that this is a function of the energy x, not of occupation(x). This function satisfies s' = x f' (see https://www.vasp.at/vasp-workshop/k-points.pdf p. 12 and https://arxiv.org/pdf/1805.07144.pdf p. 18.

source
DFTK.Smearing.occupationFunction
occupation(S::SmearingFunction, x)

Occupation at x, where in practice x = (ε - εF) / temperature. If temperature is zero, (ε-εF)/temperature = ±∞. The occupation function is required to give 1 and 0 respectively in these cases.

source
+ is_converged=ScfConvergenceDensity(tol))

Newton algorithm. Be careful that the starting point needs to be not too far from the solution.

source
DFTK.next_compatible_fft_sizeMethod

Find the next compatible FFT size Sizes must (a) be a product of small primes only and (b) contain the factors. If smallprimes is empty (a) is skipped.

source
DFTK.next_densityFunction

Obtain new density ρ by diagonalizing ham. Follows the policy imposed by the bands data structure to determine and adjust the number of bands to be computed.

source
DFTK.norm_cplxMethod

Complex-analytic extension of LinearAlgebra.norm(x) from real to complex inputs. Needed for phonons as we want to perform a matrix-vector product f'(x)·h, where f is a real-to-real function and h a complex vector. To do this using automatic differentiation, we can extend analytically f to accept complex inputs, then differentiate t -> f(x+t·h). This will fail if non-analytic functions like norm are used for complex inputs, and therefore we have to redefine it.

source
DFTK.overlap_Mmn_k_kpbMethod

Computes the matrix $[M^{k,b}]_{m,n} = \langle u_{m,k} | u_{n,k+b} \rangle$ for given k, kpb = $k+b$.

G_shift is the "shifting" vector, correction due to the periodicity conditions imposed on $k \to ψ_k$. It is non zero if kpb is taken in another unit cell of the reciprocal lattice. We use here that: $u_{n(k + G_{\rm shift})}(r) = e^{-i*\langle G_{\rm shift},r \rangle} u_{nk}$.

source
DFTK.plot_bandstructureFunction

Compute and plot the band structure. n_bands selects the number of bands to compute. If this value is absent and an scfres is used to start the calculation a default of n_bands_scf + 5sqrt(n_bands_scf) is used. The unit used to plot the bands can be selected using the unit parameter. Like in the rest of DFTK Hartree is used by default. Another standard choices is unit=u"eV" (electron volts). The kline_density is given in number of $k$-points per inverse bohrs (i.e. overall in units of length).

source
DFTK.plot_dosFunction

Plot the density of states over a reasonable range. Requires to load Plots.jl beforehand.

source
DFTK.psp_local_polynomialFunction

The local potential of a HGH pseudopotentials in reciprocal space can be brought to the form $Q(t) / (t^2 exp(t^2 / 2))$ where $t = r_\text{loc} q$ and Q is a polynomial of at most degree 8. This function returns Q.

source
DFTK.psp_projector_polynomialFunction

The nonlocal projectors of a HGH pseudopotentials in reciprocal space can be brought to the form $Q(t) exp(-t^2 / 2)$ where $t = r_l q$ and Q is a polynomial. This function returns Q.

source
DFTK.qcut_psp_localMethod

Estimate an upper bound for the argument q after which abs(eval_psp_local_fourier(psp, q)) is a strictly decreasing function.

source
DFTK.qcut_psp_projectorMethod

Estimate an upper bound for the argument q after which eval_psp_projector_fourier(psp, q) is a strictly decreasing function.

source
DFTK.r_vectorsMethod
r_vectors(basis::PlaneWaveBasis)

The list of $r$ vectors, in reduced coordinates. By convention, this is in [0,1)^3.

source
DFTK.read_w90_nnkpMethod

Read the .nnkp file provided by the preprocessing routine of Wannier90 (i.e. "wannier90.x -pp prefix") Returns:

  1. the array 'nnkpts' of k points, their respective nearest neighbors and associated shifing vectors (non zero if the neighbor is located in another cell).
  2. the number 'nntot' of neighbors per k point.

TODO: add the possibility to exclude bands

source
DFTK.run_wannier90Function

Wannerize the obtained bands using wannier90. By default all converged bands from the scfres are employed (change with n_bands kwargs) and n_wannier = n_bands wannier functions are computed using random Gaussians as guesses. All keyword arguments supported by Wannier90 for the disentanglement may be added as keyword arguments. The function returns the fileprefix.

Experimental feature

Currently this is an experimental feature, which has not yet been tested to full depth. The interface is considered unstable and may change incompatibly in the future. Use at your own risk and please report bugs in case you encounter any.

source
DFTK.save_scfresMethod
save_scfres(filename, scfres)

Save an scfres obtained from self_consistent_field to a file. The format is determined from the file extension. Currently the following file extensions are recognized and supported:

  • jld2: A JLD2 file. Stores the complete state and can be used (with load_scfres) to restart an SCF from a checkpoint or post-process an SCF solution. See Saving SCF results on disk and SCF checkpoints for details.
  • vts: A VTK file for visualisation e.g. in paraview. Stores the density, spin density and some metadata (energy, Fermi level, occupation etc.). Supports these keyword arguments:
    • save_ψ: Save the real-space representation of the orbitals as well (may lead to larger files).
    • extra_data: Dict{String,Array} with additional data on the 3D real-space grid to store into the VTK file.
  • json: A JSON file with basic information about the SCF run. Stores for example the number of iterations, occupations, norm of the most recent density change, eigenvalues, Fermi level etc.
No compatibility guarantees

No guarantees are made with respect to this function at this point. It may change incompatibly between DFTK versions or stop working / be removed in the future.

source
DFTK.scf_damping_quadratic_modelMethod

Use the two iteration states info and info_next to find a damping value from a quadratic model for the SCF energy. Returns nothing if the constructed model is not considered trustworthy, else returns the suggested damping.

source
DFTK.self_consistent_fieldMethod
self_consistent_field(basis; [tol, mixing, damping, ρ, ψ])

Solve the Kohn-Sham equations with a density-based SCF algorithm using damped, preconditioned iterations where $ρ_\text{next} = α P^{-1} (ρ_\text{out} - ρ_\text{in})$.

Overview of parameters:

  • ρ: Initial density
  • ψ: Initial orbitals
  • tol: Tolerance for the density change ($\|ρ_\text{out} - ρ_\text{in}\|$) to flag convergence. Default is 1e-6.
  • is_converged: Convergence control callback. Typical objects passed here are DFTK.ScfConvergenceDensity(tol) (the default), DFTK.ScfConvergenceEnergy(tol) or DFTK.ScfConvergenceForce(tol).
  • maxiter: Maximal number of SCF iterations
  • mixing: Mixing method, which determines the preconditioner $P^{-1}$ in the above equation. Typical mixings are LdosMixing, KerkerMixing, SimpleMixing or DielectricMixing. Default is LdosMixing()
  • damping: Damping parameter $α$ in the above equation. Default is 0.8.
  • nbandsalg: By default DFTK uses nbandsalg=AdaptiveBands(model), which adaptively determines the number of bands to compute. If you want to influence this algorithm or use a predefined number of bands in each SCF step, pass a FixedBands or AdaptiveBands. Beware that with non-zero temperature, the convergence of the SCF algorithm may be limited by the default_occupation_threshold parameter. For highly accurate calculations we thus recommend increasing the default_occupation_threshold of the AdaptiveBands.
  • callback: Function called at each SCF iteration. Usually takes care of printing the intermediate state.
source
DFTK.solve_ΩplusKMethod
solve_ΩplusK(basis::PlaneWaveBasis{T}, ψ, res, occupation;
+             tol=1e-10, verbose=false) where {T}

Return δψ where (Ω+K) δψ = rhs

source
DFTK.solve_ΩplusK_splitMethod

Solve the problem (Ω+K) δψ = rhs using a split algorithm, where rhs is typically -δHextψ (the negative matvec of an external perturbation with the SCF orbitals ψ) and δψ is the corresponding total variation in the orbitals ψ. Additionally returns: - δρ: Total variation in density) - δHψ: Total variation in Hamiltonian applied to orbitals - δeigenvalues: Total variation in eigenvalues - δVind: Change in potential induced by δρ (the term needed on top of δHextψ to get δHψ).

source
DFTK.spglib_standardize_cellMethod

Returns crystallographic conventional cell according to the International Table of Crystallography Vol A (ITA) in case primitive=false. If primitive=true the primitive lattice is returned in the convention of the reference work of Cracknell, Davies, Miller, and Love (CDML). Of note this has minor differences to the primitive setting choice made in the ITA.

source
DFTK.sphericalbesselj_fastMethod
sphericalbesselj_fast(l::Integer, x::Number)

Returns the spherical Bessel function of the first kind jl(x). Consistent with https://en.wikipedia.org/wiki/Besselfunction#SphericalBesselfunctions and with SpecialFunctions.sphericalbesselj. Specialized for integer 0 <= l <= 5.

source
DFTK.standardize_atomsFunction

Apply various standardisations to a lattice and a list of atoms. It uses spglib to detect symmetries (within tol_symmetry), then cleans up the lattice according to the symmetries (unless correct_symmetry is false) and returns the resulting standard lattice and atoms. If primitive is true (default) the primitive unit cell is returned, else the conventional unit cell is returned.

source
DFTK.symmetrize_ρMethod

Symmetrize a density by applying all the basis (by default) symmetries and forming the average.

source
DFTK.symmetry_operationsFunction

Return the symmetries given an atomic structure with optionally designated magnetic moments on each of the atoms. The symmetries are determined using spglib.

source
DFTK.symmetry_operationsMethod

Return the Symmetry operations given a hall_number.

This function allows to directly access to the space group operations in the spglib database. To specify the space group type with a specific choice, hall_number is used.

The definition of hall_number is found at Space group type.

source
DFTK.synchronize_deviceMethod

Synchronize data and finish all operations on the execution stream of the device. This needs to be called explicitly before a task finishes (e.g. in an @spawn block).

source
DFTK.to_cpuMethod

Transfer an array from a device (typically a GPU) to the CPU.

source
DFTK.todictMethod

Convert an Energies struct to a dictionary representation

source
DFTK.transfer_blochwave_kptMethod

Transfer an array ψk_in expanded on kpt_in, and produce $ψ(r) e^{i ΔG·r}$ expanded on kpt_out. It is mostly useful for phonons. Beware: ψk_out can lose information if the shift ΔG is large or if the G_vectors differ between k-points.

source
DFTK.transfer_densityMethod

Transfer density (in real space) between two basis sets.

This function is fast by transferring only the Fourier coefficients from the small basis to the big basis.

Note that this implies that for even-sized small FFT grids doing the transfer small -> big -> small is not an identity (as the small basis has an unmatched Fourier component and the identity $c_G = c_{-G}^\ast$ does not fully hold).

Note further that for the direction big -> small employing this function does not give the same answer as using first transfer_blochwave and then compute_density.

source
DFTK.transfer_mappingMethod

Compute the index mapping between the global grids of two bases. Returns an iterator of 8 pairs (block_in, block_out). Iterated over these pairs x_out_fourier[block_out, :] = x_in_fourier[block_in, :] does the transfer from the Fourier coefficients x_in_fourier (defined on basis_in) to x_out_fourier (defined on basis_out, equally provided as Fourier coefficients).

source
DFTK.transfer_mappingMethod

Compute the index mapping between two bases. Returns two arrays idcs_in and idcs_out such that ψkout[idcs_out] = ψkin[idcs_in] does the transfer from ψkin (defined on basis_in and kpt_in) to ψkout (defined on basis_out and kpt_out).

source
DFTK.unfold_bzMethod

" Convert a basis into one that doesn't use BZ symmetry. This is mainly useful for debug purposes (e.g. in cases we don't want to bother thinking about symmetries).

source
DFTK.versioninfoFunction
DFTK.versioninfo([io::IO=stdout])

Summary of version and configuration of DFTK and its key dependencies.

source
DFTK.write_w90_winMethod

Write a win file at the indicated prefix. Parameters to Wannier90 can be added as kwargs: e.g. num_iter=500.

source
DFTK.ylm_realMethod

Returns the (l,m) real spherical harmonic Ylm(r). Consistent with https://en.wikipedia.org/wiki/Tableofsphericalharmonics#Realsphericalharmonics

source
DFTK.zeros_likeFunction

Create an array of same "array type" as X filled with zeros, minimizing the number of allocations. This unifies CPU and GPU code, as the output will always be on the same device as the input.

source
DFTK.@timingMacro

Shortened version of the @timeit macro from TimerOutputs, which writes to the DFTK timer.

source
DFTK.Smearing.entropyMethod

Entropy. Note that this is a function of the energy x, not of occupation(x). This function satisfies s' = x f' (see https://www.vasp.at/vasp-workshop/k-points.pdf p. 12 and https://arxiv.org/pdf/1805.07144.pdf p. 18.

source
DFTK.Smearing.occupationFunction
occupation(S::SmearingFunction, x)

Occupation at x, where in practice x = (ε - εF) / temperature. If temperature is zero, (ε-εF)/temperature = ±∞. The occupation function is required to give 1 and 0 respectively in these cases.

source
diff --git a/dev/developer/conventions/index.html b/dev/developer/conventions/index.html index a9a9b588b5..c943b8d48a 100644 --- a/dev/developer/conventions/index.html +++ b/dev/developer/conventions/index.html @@ -3,4 +3,4 @@ using UnitfulAtomic austrip(10u"eV") # 10eV in Hartree
0.36749322175518595
using Unitful: Å
 using UnitfulAtomic
-auconvert(Å, 1.2)  # 1.2 Bohr in Ångström
0.6350126530835999 Å
Differing unit conventions

Different electronic-structure codes use different unit conventions. For example for lattice vectors the common length units are Bohr (used by DFTK) and Ångström (used e.g. by ASE, 1Å ≈ 1.80 Bohr). When setting up a calculation for DFTK one needs to ensure to convert to Bohr and atomic units. When structures are provided as AtomsBase.jl-compatible objects, this unit conversion is automatically performed behind the scenes. See AtomsBase integration for details.

Lattices and lattice vectors

Both the real-space lattice (i.e. model.lattice) and reciprocal-space lattice (model.recip_lattice) contain the lattice vectors in columns. For example, model.lattice[:, 1] is the first real-space lattice vector. If 1D or 2D problems are to be treated these arrays are still $3 \times 3$ matrices, but contain two or one zero-columns, respectively. The real-space lattice vectors are sometimes referred to by $A$ and the reciprocal-space lattice vectors by $B = 2\pi A^{-T}$.

Row-major versus column-major storage order

Julia stores matrices as column-major, but other languages (notably Python and C) use row-major ordering. Care therefore needs to be taken to properly transpose the unit cell matrices $A$ before using it with DFTK. For the supported third-party packages load_lattice, load_positions and load_atoms again handle such conversion automatically.

We use the convention that the unit cell in real space is $[0, 1)^3$ in reduced coordinates and the unit cell in reciprocal space (the reducible Brillouin zone) is $[-1/2, 1/2)^3$.

Reduced and cartesian coordinates

Unless denoted otherwise the code uses reduced coordinates for reciprocal-space vectors such as $k$, $G$, $q$ or real-space vectors like $r$ and $R$ (see Symbol conventions). One switches to Cartesian coordinates by

\[x_\text{cart} = M x_\text{red}\]

where $M$ is either $A$ / model.lattice (for real-space vectors) or $B$ / model.recip_lattice (for reciprocal-space vectors). A useful relationship is

\[b_\text{cart} \cdot a_\text{cart}=2\pi b_\text{red} \cdot a_\text{red}\]

if $a$ and $b$ are real-space and reciprocal-space vectors respectively. Other names for reduced coordinates are integer coordinates (usually for $G$-vectors) or fractional coordinates (usually for $k$-points).

Normalization conventions

The normalization conventions used in the code is that quantities stored in reciprocal space are coefficients in the $e_{G}$ basis, and quantities stored in real space use real physical values. This means for instance that wavefunctions in the real space grid are normalized as $\frac{|\Omega|}{N} \sum_{r} |\psi(r)|^{2} = 1$ where $N$ is the number of grid points and in reciprocal space its coefficients are $\ell^{2}$-normalized, see the discussion in section PlaneWaveBasis and plane-wave discretisations where this is demonstrated.

+auconvert(Å, 1.2) # 1.2 Bohr in Ångström
0.6350126530835999 Å
Differing unit conventions

Different electronic-structure codes use different unit conventions. For example for lattice vectors the common length units are Bohr (used by DFTK) and Ångström (used e.g. by ASE, 1Å ≈ 1.80 Bohr). When setting up a calculation for DFTK one needs to ensure to convert to Bohr and atomic units. When structures are provided as AtomsBase.jl-compatible objects, this unit conversion is automatically performed behind the scenes. See AtomsBase integration for details.

Lattices and lattice vectors

Both the real-space lattice (i.e. model.lattice) and reciprocal-space lattice (model.recip_lattice) contain the lattice vectors in columns. For example, model.lattice[:, 1] is the first real-space lattice vector. If 1D or 2D problems are to be treated these arrays are still $3 \times 3$ matrices, but contain two or one zero-columns, respectively. The real-space lattice vectors are sometimes referred to by $A$ and the reciprocal-space lattice vectors by $B = 2\pi A^{-T}$.

Row-major versus column-major storage order

Julia stores matrices as column-major, but other languages (notably Python and C) use row-major ordering. Care therefore needs to be taken to properly transpose the unit cell matrices $A$ before using it with DFTK. For the supported third-party packages load_lattice, load_positions and load_atoms again handle such conversion automatically.

We use the convention that the unit cell in real space is $[0, 1)^3$ in reduced coordinates and the unit cell in reciprocal space (the reducible Brillouin zone) is $[-1/2, 1/2)^3$.

Reduced and cartesian coordinates

Unless denoted otherwise the code uses reduced coordinates for reciprocal-space vectors such as $k$, $G$, $q$ or real-space vectors like $r$ and $R$ (see Symbol conventions). One switches to Cartesian coordinates by

\[x_\text{cart} = M x_\text{red}\]

where $M$ is either $A$ / model.lattice (for real-space vectors) or $B$ / model.recip_lattice (for reciprocal-space vectors). A useful relationship is

\[b_\text{cart} \cdot a_\text{cart}=2\pi b_\text{red} \cdot a_\text{red}\]

if $a$ and $b$ are real-space and reciprocal-space vectors respectively. Other names for reduced coordinates are integer coordinates (usually for $G$-vectors) or fractional coordinates (usually for $k$-points).

Normalization conventions

The normalization conventions used in the code is that quantities stored in reciprocal space are coefficients in the $e_{G}$ basis, and quantities stored in real space use real physical values. This means for instance that wavefunctions in the real space grid are normalized as $\frac{|\Omega|}{N} \sum_{r} |\psi(r)|^{2} = 1$ where $N$ is the number of grid points and in reciprocal space its coefficients are $\ell^{2}$-normalized, see the discussion in section PlaneWaveBasis and plane-wave discretisations where this is demonstrated.

diff --git a/dev/developer/data_structures/3d81dbdf.svg b/dev/developer/data_structures/3d81dbdf.svg deleted file mode 100644 index 20729b6ce5..0000000000 --- a/dev/developer/data_structures/3d81dbdf.svg +++ /dev/null @@ -1,75 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/dev/developer/data_structures/476e29eb.svg b/dev/developer/data_structures/476e29eb.svg new file mode 100644 index 0000000000..3d4ea1601d --- /dev/null +++ b/dev/developer/data_structures/476e29eb.svg @@ -0,0 +1,75 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/developer/data_structures/e433876b.svg b/dev/developer/data_structures/4c8fd027.svg similarity index 62% rename from dev/developer/data_structures/e433876b.svg rename to dev/developer/data_structures/4c8fd027.svg index 99e73b50c5..b9eed7da5e 100644 --- a/dev/developer/data_structures/e433876b.svg +++ b/dev/developer/data_structures/4c8fd027.svg @@ -1,5671 +1,5671 @@ - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/developer/data_structures/index.html b/dev/developer/data_structures/index.html index 49c20f5c7f..eb9f2fc244 100644 --- a/dev/developer/data_structures/index.html +++ b/dev/developer/data_structures/index.html @@ -7,19 +7,19 @@ PspCorrection Hartree Xc

DFTK computes energies for all terms of the model individually, which are available in scfres.energies:

scfres.energies
Energy breakdown (in Ha):
-    Kinetic             3.1739330 
-    AtomicLocal         -2.1467804
-    AtomicNonlocal      1.5858704 
+    Kinetic             3.1739301 
+    AtomicLocal         -2.1467790
+    AtomicNonlocal      1.5858721 
     Ewald               -8.4004648
     PspCorrection       -0.2948928
-    Hartree             0.5586697 
-    Xc                  -2.4032003
+    Hartree             0.5586694 
+    Xc                  -2.4032002
 
-    total               -7.926865085166

For now the following energy terms are available in DFTK:

Custom types can be added if needed. For examples see the definition of the above terms in the src/terms directory.

By mixing and matching these terms, the user can create custom models not limited to DFT. Convenience constructors are provided for common cases:

PlaneWaveBasis and plane-wave discretisations

The PlaneWaveBasis datastructure handles the discretization of a given Model in a plane-wave basis. In plane-wave methods the discretization is twofold: Once the $k$-point grid, which determines the sampling inside the Brillouin zone and on top of that a finite plane-wave grid to discretise the lattice-periodic functions. The former aspect is controlled by the kgrid argument of PlaneWaveBasis, the latter is controlled by the cutoff energy parameter Ecut:

PlaneWaveBasis(model; Ecut, kgrid)
PlaneWaveBasis discretization:
+    total               -7.926865085712

For now the following energy terms are available in DFTK:

Custom types can be added if needed. For examples see the definition of the above terms in the src/terms directory.

By mixing and matching these terms, the user can create custom models not limited to DFT. Convenience constructors are provided for common cases:

PlaneWaveBasis and plane-wave discretisations

The PlaneWaveBasis datastructure handles the discretization of a given Model in a plane-wave basis. In plane-wave methods the discretization is twofold: Once the $k$-point grid, which determines the sampling inside the Brillouin zone and on top of that a finite plane-wave grid to discretise the lattice-periodic functions. The former aspect is controlled by the kgrid argument of PlaneWaveBasis, the latter is controlled by the cutoff energy parameter Ecut:

PlaneWaveBasis(model; Ecut, kgrid)
PlaneWaveBasis discretization:
     architecture         : DFTK.CPU()
     num. mpi processes   : 1
     num. julia threads   : 1
-    num. blas  threads   : 2
+    num. blas  threads   : 1
     num. fft   threads   : 1
 
     Ecut                 : 15.0 Ha
@@ -53,8 +53,8 @@
   &= \sum_{G \in \mathcal R^{*}} c_{G}  e^{i  k \cdot  x} e_{G}(x)
 \end{aligned}\]

where $\mathcal R^*$ is the set of reciprocal lattice vectors. The $c_{{G}}$ are $\ell^{2}$-normalized. The summation is truncated to a "spherical", $k$-dependent basis set

\[ S_{k} = \left\{G \in \mathcal R^{*} \,\middle|\, \frac 1 2 |k+ G|^{2} \le E_\text{cut}\right\}\]

where $E_\text{cut}$ is the cutoff energy.

Densities involve terms like $|\psi_{k}|^{2} = |u_{k}|^{2}$ and therefore products $e_{-{G}} e_{{G}'}$ for ${G}, {G}'$ in $S_{k}$. To represent these we use a "cubic", $k$-independent basis set large enough to contain the set $\{{G}-G' \,|\, G, G' \in S_{k}\}$. We can obtain the coefficients of densities on the $e_{G}$ basis by a convolution, which can be performed efficiently with FFTs (see ifft and fft functions). Potentials are discretized on this same set.

The normalization conventions used in the code is that quantities stored in reciprocal space are coefficients in the $e_{G}$ basis, and quantities stored in real space use real physical values. This means for instance that wavefunctions in the real space grid are normalized as $\frac{|\Omega|}{N} \sum_{r} |\psi(r)|^{2} = 1$ where $N$ is the number of grid points.

For example let us check the normalization of the first eigenfunction at the first $k$-point in reciprocal space:

ψtest = scfres.ψ[1][:, 1]
-sum(abs2.(ψtest))
1.0000000000000007

We now perform an IFFT to get ψ in real space. The $k$-point has to be passed because ψ is expressed on the $k$-dependent basis. Again the function is normalised:

ψreal = ifft(basis, basis.kpoints[1], ψtest)
-sum(abs2.(ψreal)) * basis.dvol
1.0000000000000004

The list of $k$ points of the basis can be obtained with basis.kpoints.

basis.kpoints
8-element Vector{Kpoint{Float64, Vector{StaticArraysCore.SVector{3, Int64}}}}:
+sum(abs2.(ψtest))
1.000000000000001

We now perform an IFFT to get ψ in real space. The $k$-point has to be passed because ψ is expressed on the $k$-dependent basis. Again the function is normalised:

ψreal = ifft(basis, basis.kpoints[1], ψtest)
+sum(abs2.(ψreal)) * basis.dvol
1.0000000000000009

The list of $k$ points of the basis can be obtained with basis.kpoints.

basis.kpoints
8-element Vector{Kpoint{Float64, Vector{StaticArraysCore.SVector{3, Int64}}}}:
  KPoint([     0,      0,      0], spin = 1, num. G vectors =   725)
  KPoint([  0.25,      0,      0], spin = 1, num. G vectors =   754)
  KPoint([  -0.5,      0,      0], spin = 1, num. G vectors =   754)
@@ -85,6 +85,6 @@
  [0.06666666666666667, 0.0, 0.0]
  [0.1, 0.0, 0.0]

Accessing Bloch waves and densities

Wavefunctions are stored in an array scfres.ψ as ψ[ik][iG, iband] where ik is the index of the $k$-point (in basis.kpoints), iG is the index of the plane wave (in G_vectors(basis, basis.kpoints[ik])) and iband is the index of the band. Densities are stored in real space, as a 4-dimensional array (the third being the spin component).

rvecs = collect(r_vectors(basis))[:, 1, 1]  # slice along the x axis
 x = [r[1] for r in rvecs]                   # only keep the x coordinate
-plot(x, scfres.ρ[:, 1, 1, 1], label="", xlabel="x", ylabel="ρ", marker=2)
Example block output
G_energies = [sum(abs2.(model.recip_lattice * G)) ./ 2 for G in G_vectors(basis)][:]
+plot(x, scfres.ρ[:, 1, 1, 1], label="", xlabel="x", ylabel="ρ", marker=2)
Example block output
G_energies = [sum(abs2.(model.recip_lattice * G)) ./ 2 for G in G_vectors(basis)][:]
 scatter(G_energies, abs.(fft(basis, scfres.ρ)[:]);
-        yscale=:log10, ylims=(1e-12, 1), label="", xlabel="Energy", ylabel="|ρ|")
Example block output

Note that the density has no components on wavevectors above a certain energy, because the wavefunctions are limited to $\frac 1 2|k+G|^2 ≤ E_{\rm cut}$.

  • 2If you are not familiar with Julia syntax, typeof.(model.term_types) is equivalent to [typeof(t) for t in model.term_types].
+ yscale=:log10, ylims=(1e-12, 1), label="", xlabel="Energy", ylabel="|ρ|")
Example block output

Note that the density has no components on wavevectors above a certain energy, because the wavefunctions are limited to $\frac 1 2|k+G|^2 ≤ E_{\rm cut}$.

diff --git a/dev/developer/gpu_computations/index.html b/dev/developer/gpu_computations/index.html index 322248d36b..0d3377720d 100644 --- a/dev/developer/gpu_computations/index.html +++ b/dev/developer/gpu_computations/index.html @@ -17,4 +17,4 @@ map(Gs) do Gi model.lattice * Gi end -end

Instead, we should use map which returns an array of the same type as the input one.

performance. For example, iterating through the columns of a matrix to compute their norms is not efficient, as a new kernel is launched for every column. Instead, it is better to build the vector containing these norms, as it is a vectorized operation and will be much faster on the GPU.

+end

Instead, we should use map which returns an array of the same type as the input one.

performance. For example, iterating through the columns of a matrix to compute their norms is not efficient, as a new kernel is launched for every column. Instead, it is better to build the vector containing these norms, as it is a vectorized operation and will be much faster on the GPU.

diff --git a/dev/developer/setup/index.html b/dev/developer/setup/index.html index aaa9ac26fb..ed50bb1e7c 100644 --- a/dev/developer/setup/index.html +++ b/dev/developer/setup/index.html @@ -2,4 +2,4 @@ Developer setup · DFTK.jl

Developer setup

Source code installation

If you want to start developing DFTK it is highly recommended that you setup the sources in a way such that Julia can automatically keep track of your changes to the DFTK code files during your development. This means you should not Pkg.add your package, but use Pkg.develop instead. With this setup also tools such as Revise.jl can work properly. Note that using Revise.jl is highly recommended since this package automatically refreshes changes to the sources in an active Julia session (see its docs for more details).

To achieve such a setup you have two recommended options:

  1. Clone DFTK into a location of your choice

    $ git clone https://github.com/JuliaMolSim/DFTK.jl /some/path/

    Whenever you want to use exactly this development version of DFTK in a Julia environment (e.g. the global one) add it as a develop package:

    import Pkg
     Pkg.develop("/some/path/")

    To run a script or start a Julia REPL using exactly this source tree as the DFTK version, use the --project flag of Julia, see this documentation for details. For example to start a Julia REPL with this version of DFTK use

    $ julia --project=/some/path/

    The advantage of this method is that you can easily have multiple clones of DFTK with potentially different modifications made.

  2. Add a development version of DFTK to the global Julia environment:

    import Pkg
     Pkg.develop("DFTK")

    This clones DFTK to the path ~/.julia/dev/DFTK" (on Linux). Note that with this method you cannot install both the stable and the development version of DFTK into your global environment.

Disabling precompilation

For the best experience in using DFTK we employ PrecompileTools.jl to reduce the time to first SCF. However, spending the additional time for precompiling DFTK is usually not worth it during development. We therefore recommend disabling precompilation in a development setup. See the PrecompileTools documentation for detailed instructions how to do this.

At the time of writing dropping a file LocalPreferences.toml in DFTK's root folder (next to the Project.toml) with the following contents is sufficient:

[DFTK]
-precompile_workload = false
+precompile_workload = false diff --git a/dev/developer/symmetries/index.html b/dev/developer/symmetries/index.html index f128670f2c..cc0b812e85 100644 --- a/dev/developer/symmetries/index.html +++ b/dev/developer/symmetries/index.html @@ -15,44 +15,43 @@ basis_nosym = PlaneWaveBasis(model_nosym; Ecut, kgrid) scfres_nosym = @time self_consistent_field(basis_nosym, tol=1e-6)
n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime
 ---   ---------------   ---------   ---------   ----   ------
-  1   -7.864627139822                   -0.72    3.4
-  2   -7.868968672112       -2.36       -1.54    1.0    139ms
-  3   -7.869174538062       -3.69       -2.60    1.1    145ms
-  4   -7.869209105852       -4.46       -2.89    2.4    230ms
-  5   -7.869209578172       -6.33       -3.12    1.0    142ms
-  6   -7.869209808054       -6.64       -4.17    1.0    183ms
-  7   -7.869209817322       -8.03       -4.98    1.9    194ms
-  8   -7.869209817517       -9.71       -5.25    1.9    204ms
-  9   -7.869209817529      -10.92       -5.82    1.0    147ms
- 10   -7.869209817530      -12.02       -6.14    1.5    222ms
-  1.969440 seconds (1.61 M allocations: 717.676 MiB, 6.94% gc time)

and then redo it using symmetry (the default):

model_sym = model_LDA(lattice, atoms, positions)
+  1   -7.864626880341                   -0.72    3.4
+  2   -7.868965162897       -2.36       -1.54    1.0    314ms
+  3   -7.869174691624       -3.68       -2.60    1.1    332ms
+  4   -7.869209077830       -4.46       -2.89    2.4    573ms
+  5   -7.869209582263       -6.30       -3.13    1.0    314ms
+  6   -7.869209809385       -6.64       -4.37    1.0    314ms
+  7   -7.869209817433       -8.09       -5.00    2.1    475ms
+  8   -7.869209817523      -10.04       -5.43    1.5    468ms
+  9   -7.869209817530      -11.18       -6.28    1.3    364ms
+  3.785173 seconds (1.46 M allocations: 650.528 MiB, 4.35% gc time)

and then redo it using symmetry (the default):

model_sym = model_LDA(lattice, atoms, positions)
 basis_sym = PlaneWaveBasis(model_sym; Ecut, kgrid)
 scfres_sym = @time self_consistent_field(basis_sym, tol=1e-6)
n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime
 ---   ---------------   ---------   ---------   ----   ------
-  1   -7.864410091484                   -0.72    4.2
-  2   -7.868978614902       -2.34       -1.54    1.0   25.1ms
-  3   -7.869174819858       -3.71       -2.61    1.1   26.4ms
-  4   -7.869209244452       -4.46       -2.88    2.4   35.2ms
-  5   -7.869209485526       -6.62       -2.97    1.0   25.3ms
-  6   -7.869209809303       -6.49       -4.06    1.0   25.8ms
-  7   -7.869209817271       -8.10       -5.06    2.0   33.6ms
-  8   -7.869209817523       -9.60       -5.35    1.9   34.2ms
-  9   -7.869209817530      -11.19       -5.79    1.0   26.2ms
- 10   -7.869209817529   +  -12.33       -5.73    1.4   28.4ms
- 11   -7.869209817531      -11.93       -7.02    1.0   26.6ms
-  0.341979 seconds (264.98 k allocations: 138.706 MiB)

Clearly both yield the same energy but the version employing symmetry is faster, since less $k$-points are explicitly treated:

(length(basis_sym.kpoints), length(basis_nosym.kpoints))
(8, 64)

Both SCFs would even agree in the convergence history if exact diagonalization was used for the eigensolver in each step of both SCFs. But since DFTK adjusts this diagtol value adaptively during the SCF to increase performance, a slightly different history is obtained. Try adding the keyword argument determine_diagtol=(args...; kwargs...) -> 1e-8 in each SCF call to fix the diagonalization tolerance to be 1e-8 for all SCF steps, which will result in an almost identical convergence history.

We can also explicitly verify both methods to yield the same density:

(norm(scfres_sym.ρ - scfres_nosym.ρ),
- norm(values(scfres_sym.energies) .- values(scfres_nosym.energies)))
(4.443261890463775e-7, 1.1418950684758973e-6)

The symmetries can be used to map reducible to irreducible points:

ikpt_red = rand(1:length(basis_nosym.kpoints))
+  1   -7.864469098590                   -0.72    4.2
+  2   -7.868984945124       -2.35       -1.54    1.0   53.0ms
+  3   -7.869174512263       -3.72       -2.61    1.0   54.1ms
+  4   -7.869209161596       -4.46       -2.88    2.5   85.4ms
+  5   -7.869209501389       -6.47       -2.99    1.1   73.8ms
+  6   -7.869209808393       -6.51       -4.04    1.0   54.6ms
+  7   -7.869209817268       -8.05       -5.07    1.6   68.2ms
+  8   -7.869209817521       -9.60       -5.27    2.0   77.4ms
+  9   -7.869209817514   +  -11.17       -5.16    1.1   57.8ms
+ 10   -7.869209817529      -10.81       -5.77    1.0   55.7ms
+ 11   -7.869209817530      -12.00       -6.19    1.1   60.0ms
+  0.832059 seconds (263.79 k allocations: 138.699 MiB, 7.36% gc time)

Clearly both yield the same energy but the version employing symmetry is faster, since less $k$-points are explicitly treated:

(length(basis_sym.kpoints), length(basis_nosym.kpoints))
(8, 64)

Both SCFs would even agree in the convergence history if exact diagonalization was used for the eigensolver in each step of both SCFs. But since DFTK adjusts this diagtol value adaptively during the SCF to increase performance, a slightly different history is obtained. Try adding the keyword argument determine_diagtol=(args...; kwargs...) -> 1e-8 in each SCF call to fix the diagonalization tolerance to be 1e-8 for all SCF steps, which will result in an almost identical convergence history.

We can also explicitly verify both methods to yield the same density:

(norm(scfres_sym.ρ - scfres_nosym.ρ),
+ norm(values(scfres_sym.energies) .- values(scfres_nosym.energies)))
(5.071503086834609e-7, 1.997196471212491e-7)

The symmetries can be used to map reducible to irreducible points:

ikpt_red = rand(1:length(basis_nosym.kpoints))
 # find a (non-unique) corresponding irreducible point in basis_nosym,
 # and the symmetry that relates them
 ikpt_irred, symop = DFTK.unfold_mapping(basis_sym, basis_nosym.kpoints[ikpt_red])
 [basis_sym.kpoints[ikpt_irred].coordinate symop.S * basis_nosym.kpoints[ikpt_red].coordinate]
3×2 StaticArraysCore.SMatrix{3, 2, Float64, 6} with indices SOneTo(3)×SOneTo(2):
- -0.5   0.0
-  0.25  0.25
-  0.0   0.5

The eigenvalues match also:

[scfres_sym.eigenvalues[ikpt_irred] scfres_nosym.eigenvalues[ikpt_red]]
7×2 Matrix{Float64}:
- -0.0678641  -0.0678642
-  0.0340215   0.0340214
-  0.131311    0.131311
-  0.179372    0.179372
-  0.319769    0.319769
-  0.428182    0.428189
-  0.482893    0.476572
+ 0.0 0.0 + 0.0 0.0 + 0.0 0.0

The eigenvalues match also:

[scfres_sym.eigenvalues[ikpt_irred] scfres_nosym.eigenvalues[ikpt_red]]
7×2 Matrix{Float64}:
+ -0.168268  -0.168268
+  0.262162   0.262162
+  0.262162   0.262162
+  0.262162   0.262162
+  0.356193   0.356193
+  0.356193   0.356193
+  0.356193   0.356193
diff --git a/dev/developer/useful_formulas/index.html b/dev/developer/useful_formulas/index.html index 9a18e5cf7a..535e63a18a 100644 --- a/dev/developer/useful_formulas/index.html +++ b/dev/developer/useful_formulas/index.html @@ -14,4 +14,4 @@ \end{aligned}\]

This also holds true for real spherical harmonics.

Spherical harmonics

+ = \delta_{l,l'} \delta_{m,m'}\]

This also holds true for real spherical harmonics.

  • Spherical harmonics parity

    \[Y_l^m(-r) = (-1)^l Y_l^m(r)\]

    This also holds true for real spherical harmonics.

  • diff --git a/dev/examples/anyons.ipynb b/dev/examples/anyons.ipynb index c9ba290c0b..ea64e9635e 100644 --- a/dev/examples/anyons.ipynb +++ b/dev/examples/anyons.ipynb @@ -21,347 +21,425 @@ "output_type": "stream", "text": [ "Iter Function value Gradient norm \n", - " 0 8.274652e+01 1.676197e+01\n", - " * time: 0.0021610260009765625\n", - " 1 6.412367e+01 9.591629e+00\n", - " * time: 0.00607609748840332\n", - " 2 5.711884e+01 1.419584e+01\n", - " * time: 0.014858007431030273\n", - " 3 4.152859e+01 9.881212e+00\n", - " * time: 0.026868104934692383\n", - " 4 3.129863e+01 8.663953e+00\n", - " * time: 0.03890419006347656\n", - " 5 2.771277e+01 7.924976e+00\n", - " * time: 0.04923105239868164\n", - " 6 1.232947e+01 2.778052e+00\n", - " * time: 0.09817314147949219\n", - " 7 7.671411e+00 3.280172e+00\n", - " * time: 0.1071779727935791\n", - " 8 7.021518e+00 1.601300e+00\n", - " * time: 0.11605310440063477\n", - " 9 6.348987e+00 2.233667e+00\n", - " * time: 0.12479019165039062\n", - " 10 6.275848e+00 4.543344e+00\n", - " * time: 0.13191509246826172\n", - " 11 5.855824e+00 2.267471e+00\n", - " * time: 0.13891911506652832\n", - " 12 5.538454e+00 1.672573e+00\n", - " * time: 0.14595508575439453\n", - " 13 5.296211e+00 2.243958e+00\n", - " * time: 0.15304207801818848\n", - " 14 5.140431e+00 1.462730e+00\n", - " * time: 0.1601090431213379\n", - " 15 5.056093e+00 1.165837e+00\n", - " * time: 0.19247198104858398\n", - " 16 4.944683e+00 1.330587e+00\n", - " * time: 0.19981908798217773\n", - " 17 4.862441e+00 6.858002e-01\n", - " * time: 0.2070941925048828\n", - " 18 4.791138e+00 6.964761e-01\n", - " * time: 0.21435904502868652\n", - " 19 4.752939e+00 1.087991e+00\n", - " * time: 0.22150111198425293\n", - " 20 4.729221e+00 3.376554e-01\n", - " * time: 0.22887015342712402\n", - " 21 4.709792e+00 2.726832e-01\n", - " * time: 0.23606419563293457\n", - " 22 4.695760e+00 1.581476e-01\n", - " * time: 0.24313712120056152\n", - " 23 4.689366e+00 1.248229e-01\n", - " * time: 0.25019001960754395\n", - " 24 4.683856e+00 1.372439e-01\n", - " * time: 0.274489164352417\n", - " 25 4.680388e+00 8.954721e-02\n", - " * time: 0.28170108795166016\n", - " 26 4.678397e+00 1.250411e-01\n", - " * time: 0.28892016410827637\n", - " 27 4.676140e+00 1.122026e-01\n", - " * time: 0.2960371971130371\n", - " 28 4.673780e+00 8.231273e-02\n", - " * time: 0.3030991554260254\n", - " 29 4.671553e+00 1.461501e-01\n", - " * time: 0.30855607986450195\n", - " 30 4.669236e+00 1.221260e-01\n", - " * time: 0.31563401222229004\n", - " 31 4.667854e+00 2.745962e-01\n", - " * time: 0.3210461139678955\n", - " 32 4.665851e+00 1.364295e-01\n", - " * time: 0.328045129776001\n", - " 33 4.663530e+00 1.313445e-01\n", - " * time: 0.34459710121154785\n", - " 34 4.660937e+00 1.214153e-01\n", - " * time: 0.3517911434173584\n", - " 35 4.660875e+00 1.808288e-01\n", - " * time: 0.3572821617126465\n", - " 36 4.658669e+00 1.565389e-01\n", - " * time: 0.3644561767578125\n", - " 37 4.656510e+00 1.179164e-01\n", - " * time: 0.3715970516204834\n", - " 38 4.654744e+00 1.245029e-01\n", - " * time: 0.3786780834197998\n", - " 39 4.653657e+00 1.400465e-01\n", - " * time: 0.38576698303222656\n", - " 40 4.652601e+00 1.028404e-01\n", - " * time: 0.3927931785583496\n", - " 41 4.651564e+00 8.305799e-02\n", - " * time: 0.399813175201416\n", - " 42 4.650971e+00 6.233618e-02\n", - " * time: 0.4067831039428711\n", - " 43 4.650385e+00 5.327854e-02\n", - " * time: 0.4234180450439453\n", - " 44 4.649962e+00 5.198455e-02\n", - " * time: 0.4306340217590332\n", - " 45 4.649778e+00 3.311828e-02\n", - " * time: 0.43780016899108887\n", - " 46 4.649575e+00 1.766484e-02\n", - " * time: 0.4449172019958496\n", - " 47 4.649483e+00 1.917647e-02\n", - " * time: 0.45200514793395996\n", - " 48 4.649420e+00 1.112692e-02\n", - " * time: 0.4590461254119873\n", - " 49 4.649410e+00 3.667913e-02\n", - " * time: 0.46442508697509766\n", - " 50 4.649369e+00 1.751438e-02\n", - " * time: 0.47141599655151367\n", - " 51 4.649345e+00 1.658181e-02\n", - " * time: 0.47841620445251465\n", - " 52 4.649324e+00 2.234384e-02\n", - " * time: 0.4950242042541504\n", - " 53 4.649302e+00 1.659619e-02\n", - " * time: 0.5021610260009766\n", - " 54 4.649286e+00 1.497570e-02\n", - " * time: 0.5093259811401367\n", - " 55 4.649273e+00 1.254045e-02\n", - " * time: 0.5164580345153809\n", - " 56 4.649262e+00 1.431908e-02\n", - " * time: 0.5235450267791748\n", - " 57 4.649256e+00 1.316237e-02\n", - " * time: 0.5305831432342529\n", - " 58 4.649250e+00 8.347788e-03\n", - " * time: 0.5375611782073975\n", - " 59 4.649245e+00 3.841088e-03\n", - " * time: 0.5445451736450195\n", - " 60 4.649241e+00 3.121563e-03\n", - " * time: 0.5515730381011963\n", - " 61 4.649240e+00 1.877572e-03\n", - " * time: 0.5680360794067383\n", - " 62 4.649239e+00 5.502678e-03\n", - " * time: 0.5734841823577881\n", - " 63 4.649239e+00 5.697020e-03\n", - " * time: 0.5789501667022705\n", - " 64 4.649238e+00 3.500227e-03\n", - " * time: 0.5860671997070312\n", - " 65 4.649237e+00 2.894992e-03\n", - " * time: 0.5932199954986572\n", - " 66 4.649236e+00 2.654817e-03\n", - " * time: 0.6002991199493408\n", - " 67 4.649234e+00 2.534840e-03\n", - " * time: 0.6073741912841797\n", - " 68 4.649234e+00 2.338817e-03\n", - " * time: 0.6144351959228516\n", - " 69 4.649233e+00 2.331857e-03\n", - " * time: 0.6214840412139893\n", - " 70 4.649233e+00 1.893324e-03\n", - " * time: 0.6284852027893066\n", - " 71 4.649232e+00 1.197592e-03\n", - " * time: 0.6450321674346924\n", - " 72 4.649232e+00 9.733918e-04\n", - " * time: 0.6521420478820801\n", - " 73 4.649232e+00 8.492411e-04\n", - " * time: 0.6593911647796631\n", - " 74 4.649232e+00 4.869388e-04\n", - " * time: 0.666499137878418\n", - " 75 4.649232e+00 4.873586e-04\n", - " * time: 0.6736290454864502\n", - " 76 4.649232e+00 5.527739e-04\n", - " * time: 0.6806631088256836\n", - " 77 4.649232e+00 5.972872e-04\n", - " * time: 0.6877281665802002\n", - " 78 4.649232e+00 3.421444e-04\n", - " * time: 0.694735050201416\n", - " 79 4.649231e+00 2.698247e-04\n", - " * time: 0.7016952037811279\n", - " 80 4.649231e+00 3.710038e-04\n", - " * time: 0.7183220386505127\n", - " 81 4.649231e+00 5.941607e-04\n", - " * time: 0.7254769802093506\n", - " 82 4.649231e+00 3.471567e-04\n", - " * time: 0.7326560020446777\n", - " 83 4.649231e+00 2.721918e-04\n", - " * time: 0.7398121356964111\n", - " 84 4.649231e+00 6.202653e-04\n", - " * time: 0.7451980113983154\n", - " 85 4.649231e+00 4.813089e-04\n", - " * time: 0.7522480487823486\n", - " 86 4.649231e+00 4.598287e-04\n", - " * time: 0.7592442035675049\n", - " 87 4.649231e+00 4.325390e-04\n", - " * time: 0.7662391662597656\n", - " 88 4.649231e+00 4.626754e-04\n", - " * time: 0.7732150554656982\n", - " 89 4.649231e+00 3.651013e-04\n", - " * time: 0.7896370887756348\n", - " 90 4.649231e+00 3.111269e-04\n", - " * time: 0.7968251705169678\n", - " 91 4.649231e+00 2.152981e-04\n", - " * time: 0.8040320873260498\n", - " 92 4.649231e+00 1.636346e-04\n", - " * time: 0.8111801147460938\n", - " 93 4.649231e+00 2.633569e-04\n", - " * time: 0.818256139755249\n", - " 94 4.649231e+00 1.575390e-04\n", - " * time: 0.8253180980682373\n", - " 95 4.649231e+00 8.666068e-05\n", - " * time: 0.832341194152832\n", - " 96 4.649231e+00 6.560057e-05\n", - " * time: 0.8394010066986084\n", - " 97 4.649231e+00 1.742150e-04\n", - " * time: 0.844763994216919\n", - " 98 4.649231e+00 2.015178e-04\n", - " * time: 0.8501121997833252\n", - " 99 4.649231e+00 1.057573e-04\n", - " * time: 0.8666000366210938\n", - " 100 4.649231e+00 1.355745e-04\n", - " * time: 0.873751163482666\n", - " 101 4.649231e+00 1.137145e-04\n", - " * time: 0.8809959888458252\n", - " 102 4.649231e+00 9.649917e-05\n", - " * time: 0.8881089687347412\n", - " 103 4.649231e+00 7.225172e-05\n", - " * time: 0.8951091766357422\n", - " 104 4.649231e+00 1.375120e-04\n", - " * time: 0.9005541801452637\n", - " 105 4.649231e+00 9.531561e-05\n", - " * time: 0.9076011180877686\n", - " 106 4.649231e+00 1.106985e-04\n", - " * time: 0.9145991802215576\n", - " 107 4.649231e+00 9.773761e-05\n", - " * time: 0.9215641021728516\n", - " 108 4.649231e+00 9.130488e-05\n", - " * time: 0.938014030456543\n", - " 109 4.649231e+00 7.117427e-05\n", - " * time: 0.9451560974121094\n", - " 110 4.649231e+00 8.314829e-05\n", - " * time: 0.950700044631958\n", - " 111 4.649231e+00 5.484999e-05\n", - " * time: 0.9577810764312744\n", - " 112 4.649231e+00 6.697343e-05\n", - " * time: 0.9648070335388184\n", - " 113 4.649231e+00 5.433049e-05\n", - " * time: 0.9718801975250244\n", - " 114 4.649231e+00 3.929632e-05\n", - " * time: 0.9788520336151123\n", - " 115 4.649231e+00 4.423720e-05\n", - " * time: 0.9858100414276123\n", - " 116 4.649231e+00 2.997977e-05\n", - " * time: 0.9911880493164062\n", - " 117 4.649231e+00 2.723435e-05\n", - " * time: 0.9965050220489502\n", - " 118 4.649231e+00 1.955605e-05\n", - " * time: 1.0129930973052979\n", - " 119 4.649231e+00 1.218517e-05\n", - " * time: 1.0201401710510254\n", - " 120 4.649231e+00 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6.177174e-04\n", + " * time: 2.7418270111083984\n", + " 129 4.649231e+00 5.090390e-04\n", + " * time: 2.759740114212036\n", + " 130 4.649231e+00 3.854541e-04\n", + " * time: 2.7777931690216064\n", + " 131 4.649231e+00 3.780941e-04\n", + " * time: 2.7952680587768555\n", + " 132 4.649231e+00 2.728323e-04\n", + " * time: 2.812854051589966\n", + " 133 4.649231e+00 4.558983e-04\n", + " * time: 2.8292391300201416\n", + " 134 4.649231e+00 3.159912e-04\n", + " * time: 2.8480751514434814\n", + " 135 4.649231e+00 3.033363e-04\n", + " * time: 2.866483211517334\n", + " 136 4.649231e+00 2.795162e-04\n", + " * time: 2.8849871158599854\n", + " 137 4.649231e+00 2.850540e-04\n", + " * time: 2.9197311401367188\n", + " 138 4.649231e+00 2.117171e-04\n", + " * time: 2.9373531341552734\n", + " 139 4.649231e+00 3.122659e-04\n", + " * time: 2.951853036880493\n", + " 140 4.649231e+00 1.445242e-04\n", + " * time: 2.9699361324310303\n", + " 141 4.649231e+00 1.657402e-04\n", + " * time: 2.987572193145752\n", + " 142 4.649231e+00 1.362492e-04\n", + " * time: 3.0053532123565674\n", + " 143 4.649231e+00 1.546724e-04\n", + " * time: 3.023864984512329\n", + " 144 4.649231e+00 1.373090e-04\n", + " * time: 3.041962146759033\n", + " 145 4.649231e+00 9.272135e-05\n", + " * time: 3.0599870681762695\n", + " 146 4.649231e+00 1.101673e-04\n", + " * time: 3.0942790508270264\n", + " 147 4.649231e+00 7.886165e-05\n", + " * time: 3.108096122741699\n", + " 148 4.649231e+00 4.390601e-05\n", + " * time: 3.126574993133545\n", + " 149 4.649231e+00 5.203617e-05\n", + " * time: 3.144458055496216\n", + " 150 4.649231e+00 4.957038e-05\n", + " * time: 3.1633381843566895\n", + " 151 4.649231e+00 3.932400e-05\n", + " * time: 3.183441162109375\n", + " 152 4.649231e+00 4.201122e-05\n", + " * time: 3.2016232013702393\n", + " 153 4.649231e+00 2.667971e-05\n", + " * time: 3.219353199005127\n", + " 154 4.649231e+00 3.191197e-05\n", + " * time: 3.237258195877075\n", + " 155 4.649231e+00 2.056677e-05\n", + " * time: 3.272106170654297\n", + " 156 4.649231e+00 1.455940e-05\n", + " * time: 3.2899010181427\n", + " 157 4.649231e+00 1.231817e-05\n", + " * time: 3.3076140880584717\n", + " 158 4.649231e+00 8.415766e-06\n", + " * time: 3.3253490924835205\n", + " 159 4.649231e+00 7.059170e-06\n", + " * time: 3.3433949947357178\n", + " 160 4.649231e+00 2.110349e-05\n", + " * time: 3.3568050861358643\n", + " 161 4.649231e+00 1.507927e-05\n", + " * time: 3.3743441104888916\n", + " 162 4.649231e+00 1.276880e-05\n", + " * time: 3.3923871517181396\n", + " 163 4.649231e+00 1.429212e-05\n", + " * time: 3.410149097442627\n", + " 164 4.649231e+00 8.712209e-06\n", + " * time: 3.4280130863189697\n", + " 165 4.649231e+00 1.439077e-05\n", + " * time: 3.458069086074829\n", + " 166 4.649231e+00 8.748311e-06\n", + " * time: 3.4757561683654785\n", + " 167 4.649231e+00 1.223410e-05\n", + " * time: 3.4936351776123047\n", + " 168 4.649231e+00 8.813876e-06\n", + " * time: 3.511728048324585\n", + " 169 4.649231e+00 5.709600e-06\n", + " * time: 3.5297582149505615\n", + " 170 4.649231e+00 7.019393e-06\n", + " * time: 3.547833204269409\n", + " 171 4.649231e+00 4.246725e-06\n", + " * time: 3.5678601264953613\n", + " 172 4.649231e+00 8.522772e-06\n", + " * time: 3.581360101699829\n", + " 173 4.649231e+00 4.411377e-06\n", + " * time: 3.599544048309326\n", + " 174 4.649231e+00 4.106124e-06\n", + " * time: 3.6173930168151855\n", + " 175 4.649231e+00 3.963702e-06\n", + " * time: 3.651501178741455\n", + " 176 4.649231e+00 3.807375e-06\n", + " * time: 3.6699302196502686\n", + " 177 4.649231e+00 3.452132e-06\n", + " * time: 3.6891322135925293\n", + " 178 4.649231e+00 3.444177e-06\n", + " * time: 3.7075819969177246\n", + " 179 4.649231e+00 2.954259e-06\n", + " * time: 3.7277581691741943\n", + " 180 4.649231e+00 2.727727e-06\n", + " * time: 3.7457761764526367\n", + " 181 4.649231e+00 1.356912e-06\n", + " * time: 3.764118194580078\n", + " 182 4.649231e+00 2.410401e-06\n", + " * time: 3.781947135925293\n", + " 183 4.649231e+00 8.768506e-07\n", + " * time: 3.799870014190674\n", + " 184 4.649231e+00 7.819125e-07\n", + " * time: 3.834645986557007\n", + " 185 4.649231e+00 6.348536e-07\n", + " * time: 3.8556621074676514\n", + " 186 4.649231e+00 1.681729e-06\n", + " * time: 3.869326114654541\n", + " 187 4.649231e+00 1.184016e-06\n", + " * time: 3.887140989303589\n", + " 188 4.649231e+00 1.220922e-06\n", + " * time: 3.90505313873291\n", + " 189 4.649231e+00 8.078870e-07\n", + " * time: 3.9228479862213135\n", + " 190 4.649231e+00 9.595422e-07\n", + " * time: 3.9406371116638184\n", + " 191 4.649231e+00 7.147492e-07\n", + " * time: 3.9590489864349365\n", + " 192 4.649231e+00 5.532840e-07\n", + " * time: 3.976821184158325\n", + " 193 4.649231e+00 3.961976e-07\n", + " * time: 4.01110315322876\n", + " 194 4.649231e+00 5.065687e-07\n", + " * time: 4.0295891761779785\n", + " 195 4.649231e+00 5.250313e-07\n", + " * time: 4.048446178436279\n", + " 196 4.649231e+00 2.895700e-07\n", + " * time: 4.066676139831543\n", + " 197 4.649231e+00 6.430079e-07\n", + " * time: 4.080249071121216\n", + " 198 4.649231e+00 4.595080e-07\n", + " * time: 4.0941691398620605\n", + " 199 4.649231e+00 2.884674e-07\n", + " * time: 4.113072156906128\n", + " 200 4.649231e+00 6.461522e-07\n", + " * time: 4.127779006958008\n", + " 201 4.649231e+00 4.384503e-07\n", + " * time: 4.145666122436523\n", + " 202 4.649231e+00 3.909290e-07\n", + " * time: 4.163443088531494\n", + " 203 4.649231e+00 3.748468e-07\n", + " * time: 4.199097156524658\n", + " 204 4.649231e+00 2.599650e-07\n", + " * time: 4.217383146286011\n", + " 205 4.649231e+00 1.989176e-07\n", + " * time: 4.235920190811157\n", + " 206 4.649231e+00 1.835005e-07\n", + " * time: 4.258784055709839\n", + " 207 4.649231e+00 2.679426e-07\n", + " * time: 4.276987075805664\n", + " 208 4.649231e+00 2.679426e-07\n", + " * time: 4.321202039718628\n", + "e(1,1) / (2π)= 1.2158634835234219\n" ] }, { @@ -373,41 +451,41 @@ "\n", "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", "\n", - "\n", + "\n", "\n" ], "image/svg+xml": [ "\n", "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", "\n", - "\n", + "\n", "\n" ] }, diff --git a/dev/examples/anyons/d136187c.svg b/dev/examples/anyons/d136187c.svg new file mode 100644 index 0000000000..c7f4893515 --- /dev/null +++ b/dev/examples/anyons/d136187c.svg @@ -0,0 +1,462 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/examples/anyons/d96178f6.svg b/dev/examples/anyons/d96178f6.svg deleted file mode 100644 index dca438380b..0000000000 --- a/dev/examples/anyons/d96178f6.svg +++ /dev/null @@ -1,460 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/dev/examples/anyons/index.html b/dev/examples/anyons/index.html index fb84b5e68c..d1120f204d 100644 --- a/dev/examples/anyons/index.html +++ b/dev/examples/anyons/index.html @@ -27,4 +27,4 @@ s = 2 E11 = π/2 * (2(s+1)/s)^((s+2)/s) * (s/(s+2))^(2(s+1)/s) * E^((s+2)/s) / β println("e(1,1) / (2π)= ", E11 / (2π)) -heatmap(scfres.ρ[:, :, 1, 1], c=:blues)Example block output +heatmap(scfres.ρ[:, :, 1, 1], c=:blues)Example block output diff --git a/dev/examples/arbitrary_floattype.ipynb b/dev/examples/arbitrary_floattype.ipynb index b2ef6652d1..598b99926c 100644 --- a/dev/examples/arbitrary_floattype.ipynb +++ b/dev/examples/arbitrary_floattype.ipynb @@ -39,12 +39,12 @@ "text": [ "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -7.900379180908 -0.70 4.6 \n", - " 2 -7.904997825623 -2.34 -1.52 1.0 1.16s\n", - " 3 -7.905176162720 -3.75 -2.53 1.1 618ms\n", - " 4 -7.905211448669 -4.45 -2.83 2.8 107ms\n", - " 5 -7.905211448669 + -Inf -2.95 1.1 77.4ms\n", - " 6 -7.905212402344 -6.02 -4.69 1.0 85.4ms\n" + " 1 -7.900407314301 -0.70 4.6 \n", + " 2 -7.904996871948 -2.34 -1.52 1.0 2.14s\n", + " 3 -7.905176639557 -3.75 -2.53 1.1 979ms\n", + " 4 -7.905210494995 -4.47 -2.83 2.6 210ms\n", + " 5 -7.905211925507 -5.84 -2.97 1.1 146ms\n", + " 6 -7.905210971832 + -6.02 -4.64 1.0 170ms\n" ] } ], @@ -82,7 +82,7 @@ { "output_type": "execute_result", "data": { - "text/plain": "Energy breakdown (in Ha):\n Kinetic 3.1021488 \n AtomicLocal -2.1988871\n AtomicNonlocal 1.7295988 \n Ewald -8.3978958\n PspCorrection -0.2946220\n Hartree 0.5530711 \n Xc -2.3986261\n\n total -7.905212402344" + "text/plain": "Energy breakdown (in Ha):\n Kinetic 3.1021426 \n AtomicLocal -2.1988692\n AtomicNonlocal 1.7295910 \n Ewald -8.3978958\n PspCorrection -0.2946220\n Hartree 0.5530660 \n Xc -2.3986239\n\n total -7.905210971832" }, "metadata": {}, "execution_count": 2 diff --git a/dev/examples/arbitrary_floattype/index.html b/dev/examples/arbitrary_floattype/index.html index 89bdd22a6d..b8d6871797 100644 --- a/dev/examples/arbitrary_floattype/index.html +++ b/dev/examples/arbitrary_floattype/index.html @@ -15,17 +15,18 @@ # Run the SCF scfres = self_consistent_field(basis, tol=1e-3);
    n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime
     ---   ---------------   ---------   ---------   ----   ------
    -  1   -7.900335311890                   -0.70    4.6
    -  2   -7.904994487762       -2.33       -1.52    1.0   33.7ms
    -  3   -7.905181407928       -3.73       -2.53    1.2   35.8ms
    -  4   -7.905210494995       -4.54       -2.86    2.6   45.7ms
    -  5   -7.905211448669       -6.02       -3.04    1.1   41.0ms

    To check the calculation has really run in Float32, we check the energies and density are expressed in this floating-point type:

    scfres.energies
    Energy breakdown (in Ha):
    -    Kinetic             3.1026382 
    -    AtomicLocal         -2.2003691
    -    AtomicNonlocal      1.7302997 
    +  1   -7.900388717651                   -0.70    4.8
    +  2   -7.904994487762       -2.34       -1.52    1.0   68.2ms
    +  3   -7.905177116394       -3.74       -2.54    1.2   72.1ms
    +  4   -7.905211448669       -4.46       -2.84    2.9   98.5ms
    +  5   -7.905210971832   +   -6.32       -2.94    1.1   85.0ms
    +  6   -7.905211448669       -6.32       -4.61    1.0   69.2ms

    To check the calculation has really run in Float32, we check the energies and density are expressed in this floating-point type:

    scfres.energies
    Energy breakdown (in Ha):
    +    Kinetic             3.1021419 
    +    AtomicLocal         -2.1988680
    +    AtomicNonlocal      1.7295909 
         Ewald               -8.3978958
         PspCorrection       -0.2946220
    -    Hartree             0.5535353 
    -    Xc                  -2.3987982
    +    Hartree             0.5530651 
    +    Xc                  -2.3986237
     
    -    total               -7.905211448669
    eltype(scfres.energies.total)
    Float32
    eltype(scfres.ρ)
    Float32
    Generic linear algebra routines

    For more unusual floating-point types (like IntervalArithmetic or DoubleFloats), which are not directly supported in the standard LinearAlgebra library of Julia one additional step is required: One needs to explicitly enable the generic versions of standard linear-algebra operations like cholesky or qr, which are needed inside DFTK by loading the GenericLinearAlgebra package in the user script (i.e. just add ad using GenericLinearAlgebra next to your using DFTK call).

    + total -7.905211448669
    eltype(scfres.energies.total)
    Float32
    eltype(scfres.ρ)
    Float32
    Generic linear algebra routines

    For more unusual floating-point types (like IntervalArithmetic or DoubleFloats), which are not directly supported in the standard LinearAlgebra library of Julia one additional step is required: One needs to explicitly enable the generic versions of standard linear-algebra operations like cholesky or qr, which are needed inside DFTK by loading the GenericLinearAlgebra package in the user script (i.e. just add ad using GenericLinearAlgebra next to your using DFTK call).

    diff --git a/dev/examples/atomsbase.ipynb b/dev/examples/atomsbase.ipynb index 4b9292c26d..019c92c4c1 100644 --- a/dev/examples/atomsbase.ipynb +++ b/dev/examples/atomsbase.ipynb @@ -73,21 +73,19 @@ "text": [ "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -7.921689802929 -0.69 5.8 \n", - " 2 -7.926165620392 -2.35 -1.22 1.0 162ms\n", - " 3 -7.926836831221 -3.17 -2.37 1.8 180ms\n", - " 4 -7.926861473793 -4.61 -3.02 2.6 193ms\n", - " 5 -7.926861633842 -6.80 -3.36 1.8 191ms\n", - " 6 -7.926861667258 -7.48 -3.74 1.9 167ms\n", - " 7 -7.926861680426 -7.88 -4.34 1.4 155ms\n", - " 8 -7.926861681746 -8.88 -4.79 2.1 178ms\n", - " 9 -7.926861681865 -9.92 -5.52 1.5 181ms\n", - " 10 -7.926861681872 -11.18 -5.98 2.1 183ms\n", - " 11 -7.926861681872 -12.41 -6.27 1.6 165ms\n", - " 12 -7.926861681873 -12.78 -7.16 1.6 172ms\n", - " 13 -7.926861681873 -14.27 -7.89 2.6 197ms\n", - " 14 -7.926861681873 -14.57 -7.78 3.0 202ms\n", - " 15 -7.926861681873 + -15.05 -8.80 1.0 160ms\n" + " 1 -7.921695413440 -0.69 5.6 \n", + " 2 -7.926167485646 -2.35 -1.22 1.0 375ms\n", + " 3 -7.926836000882 -3.17 -2.37 1.8 417ms\n", + " 4 -7.926861518535 -4.59 -2.99 2.8 501ms\n", + " 5 -7.926861626627 -6.97 -3.31 1.9 396ms\n", + " 6 -7.926861665695 -7.41 -3.71 1.5 379ms\n", + " 7 -7.926861680727 -7.82 -4.43 1.4 351ms\n", + " 8 -7.926861681832 -8.96 -5.09 2.5 475ms\n", + " 9 -7.926861681860 -10.55 -5.22 2.0 402ms\n", + " 10 -7.926861681872 -10.94 -5.90 1.0 345ms\n", + " 11 -7.926861681873 -12.02 -6.87 2.0 391ms\n", + " 12 -7.926861681873 -13.44 -7.70 2.8 462ms\n", + " 13 -7.926861681873 + -15.05 -8.03 3.1 426ms\n" ] } ], @@ -125,19 +123,19 @@ "text": [ "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -7.921692156121 -0.69 6.0 \n", - " 2 -7.926161276460 -2.35 -1.22 1.0 216ms\n", - " 3 -7.926837436298 -3.17 -2.37 1.8 204ms\n", - " 4 -7.926861514189 -4.62 -3.02 2.8 237ms\n", - " 5 -7.926861637803 -6.91 -3.37 1.6 188ms\n", - " 6 -7.926861668026 -7.52 -3.75 1.5 207ms\n", - " 7 -7.926861680562 -7.90 -4.37 1.5 175ms\n", - " 8 -7.926861681793 -8.91 -4.90 2.2 210ms\n", - " 9 -7.926861681865 -10.15 -5.36 1.9 220ms\n", - " 10 -7.926861681872 -11.14 -5.97 1.8 199ms\n", - " 11 -7.926861681873 -12.10 -6.67 1.9 201ms\n", - " 12 -7.926861681873 -13.34 -7.50 2.1 226ms\n", - " 13 -7.926861681873 -15.05 -8.57 3.1 249ms\n" + " 1 -7.921693586777 -0.69 5.9 \n", + " 2 -7.926165637401 -2.35 -1.22 1.0 347ms\n", + " 3 -7.926837003725 -3.17 -2.37 1.6 425ms\n", + " 4 -7.926861518949 -4.61 -3.02 2.9 410ms\n", + " 5 -7.926861639676 -6.92 -3.38 1.8 344ms\n", + " 6 -7.926861668636 -7.54 -3.76 1.8 345ms\n", + " 7 -7.926861680512 -7.93 -4.36 1.2 371ms\n", + " 8 -7.926861681793 -8.89 -4.91 2.2 376ms\n", + " 9 -7.926861681864 -10.15 -5.34 1.8 351ms\n", + " 10 -7.926861681871 -11.11 -5.90 1.6 340ms\n", + " 11 -7.926861681873 -11.96 -7.15 1.9 379ms\n", + " 12 -7.926861681873 -13.56 -7.47 3.5 449ms\n", + " 13 -7.926861681873 -15.05 -8.49 1.6 348ms\n" ] } ], @@ -182,13 +180,13 @@ "text": [ "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -7.921696614354 -0.69 5.8 \n", - " 2 -7.926170812194 -2.35 -1.22 1.0 184ms\n", - " 3 -7.926839056051 -3.18 -2.37 1.6 211ms\n", - " 4 -7.926864922250 -4.59 -2.98 3.0 264ms\n", - " 5 -7.926865024655 -6.99 -3.26 2.0 220ms\n", - " 6 -7.926865076974 -7.28 -3.71 1.1 169ms\n", - " 7 -7.926865091986 -7.82 -4.48 1.8 186ms\n" + " 1 -7.921673508073 -0.69 5.8 \n", + " 2 -7.926162569831 -2.35 -1.22 1.0 323ms\n", + " 3 -7.926839417051 -3.17 -2.37 1.9 441ms\n", + " 4 -7.926864920873 -4.59 -2.99 2.8 395ms\n", + " 5 -7.926865029533 -6.96 -3.29 1.6 322ms\n", + " 6 -7.926865076209 -7.33 -3.70 1.2 300ms\n", + " 7 -7.926865091852 -7.81 -4.45 1.5 359ms\n" ] } ], diff --git a/dev/examples/atomsbase/index.html b/dev/examples/atomsbase/index.html index c595708ac4..cc91a3a7c2 100644 --- a/dev/examples/atomsbase/index.html +++ b/dev/examples/atomsbase/index.html @@ -27,19 +27,19 @@ basis = PlaneWaveBasis(model; Ecut=15, kgrid=[4, 4, 4]) scfres = self_consistent_field(basis, tol=1e-8);
    n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime
     ---   ---------------   ---------   ---------   ----   ------
    -  1   -7.921702931895                   -0.69    5.6
    -  2   -7.926166362345       -2.35       -1.22    1.0    191ms
    -  3   -7.926837475741       -3.17       -2.37    1.6    170ms
    -  4   -7.926861518092       -4.62       -3.03    2.9    187ms
    -  5   -7.926861644625       -6.90       -3.40    2.0    184ms
    -  6   -7.926861670342       -7.59       -3.80    1.9    159ms
    -  7   -7.926861680416       -8.00       -4.32    1.4    167ms
    -  8   -7.926861681750       -8.87       -4.83    1.9    162ms
    -  9   -7.926861681859       -9.96       -5.25    2.0    185ms
    - 10   -7.926861681871      -10.94       -5.80    1.9    161ms
    - 11   -7.926861681873      -11.77       -6.95    1.9    169ms
    - 12   -7.926861681873      -13.26       -7.47    3.5    219ms
    - 13   -7.926861681873   +  -14.57       -8.32    1.9    177ms

    If we did not want to use ASE we could of course use any other package which yields an AbstractSystem object. This includes:

    Reading a system using AtomsIO

    using AtomsIO
    +  1   -7.921663375212                   -0.69    5.9
    +  2   -7.926161432712       -2.35       -1.22    1.0    330ms
    +  3   -7.926836658775       -3.17       -2.37    1.8    364ms
    +  4   -7.926861518085       -4.60       -3.02    2.6    437ms
    +  5   -7.926861636992       -6.92       -3.37    1.6    323ms
    +  6   -7.926861667787       -7.51       -3.74    1.8    327ms
    +  7   -7.926861680488       -7.90       -4.37    1.2    310ms
    +  8   -7.926861681779       -8.89       -4.85    2.1    396ms
    +  9   -7.926861681860      -10.09       -5.27    2.0    348ms
    + 10   -7.926861681872      -10.94       -5.95    1.8    328ms
    + 11   -7.926861681873      -12.02       -6.80    2.2    362ms
    + 12   -7.926861681873      -13.45       -7.25    2.5    422ms
    + 13   -7.926861681873      -14.75       -8.21    1.8    334ms

    If we did not want to use ASE we could of course use any other package which yields an AbstractSystem object. This includes:

    Reading a system using AtomsIO

    using AtomsIO
     
     # Read a file using [AtomsIO](https://github.com/mfherbst/AtomsIO.jl),
     # which directly yields an AbstractSystem.
    @@ -51,19 +51,19 @@
     basis  = PlaneWaveBasis(model; Ecut=15, kgrid=[4, 4, 4])
     scfres = self_consistent_field(basis, tol=1e-8);
    n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime
     ---   ---------------   ---------   ---------   ----   ------
    -  1   -7.921684706340                   -0.69    5.6
    -  2   -7.926167249614       -2.35       -1.22    1.0    156ms
    -  3   -7.926836373197       -3.17       -2.37    1.8    172ms
    -  4   -7.926861514635       -4.60       -3.01    2.9    188ms
    -  5   -7.926861631185       -6.93       -3.33    1.8    187ms
    -  6   -7.926861666236       -7.46       -3.72    1.5    152ms
    -  7   -7.926861680656       -7.84       -4.40    1.9    160ms
    -  8   -7.926861681813       -8.94       -4.96    2.1    168ms
    -  9   -7.926861681850      -10.44       -5.12    1.8    183ms
    - 10   -7.926861681871      -10.67       -5.87    1.4    153ms
    - 11   -7.926861681873      -11.85       -6.88    2.1    192ms
    - 12   -7.926861681873      -13.55       -7.36    3.1    209ms
    - 13   -7.926861681873      -14.27       -8.16    2.1    164ms

    The same could be achieved using ExtXYZ by system = Atoms(read_frame("Si.extxyz")), since the ExtXYZ.Atoms object is directly AtomsBase-compatible.

    Directly setting up a system in AtomsBase

    using AtomsBase
    +  1   -7.921708246434                   -0.69    5.6
    +  2   -7.926166672484       -2.35       -1.22    1.0    347ms
    +  3   -7.926835324928       -3.17       -2.37    1.8    369ms
    +  4   -7.926861516071       -4.58       -2.99    2.8    436ms
    +  5   -7.926861622774       -6.97       -3.29    2.0    343ms
    +  6   -7.926861665888       -7.37       -3.71    1.2    308ms
    +  7   -7.926861680777       -7.83       -4.45    1.6    319ms
    +  8   -7.926861681834       -8.98       -5.10    2.4    415ms
    +  9   -7.926861681860      -10.59       -5.22    1.9    346ms
    + 10   -7.926861681872      -10.92       -5.98    1.0    299ms
    + 11   -7.926861681873      -12.13       -7.07    2.2    353ms
    + 12   -7.926861681873      -13.62       -7.50    3.1    437ms
    + 13   -7.926861681873      -14.57       -8.02    2.1    331ms

    The same could be achieved using ExtXYZ by system = Atoms(read_frame("Si.extxyz")), since the ExtXYZ.Atoms object is directly AtomsBase-compatible.

    Directly setting up a system in AtomsBase

    using AtomsBase
     using Unitful
     using UnitfulAtomic
     
    @@ -81,13 +81,13 @@
     basis  = PlaneWaveBasis(model; Ecut=15, kgrid=[4, 4, 4])
     scfres = self_consistent_field(basis, tol=1e-4);
    n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime
     ---   ---------------   ---------   ---------   ----   ------
    -  1   -7.921673119238                   -0.69    6.0
    -  2   -7.926165716164       -2.35       -1.22    1.0    183ms
    -  3   -7.926839904585       -3.17       -2.37    1.8    180ms
    -  4   -7.926864888247       -4.60       -3.00    2.6    196ms
    -  5   -7.926865033450       -6.84       -3.31    1.8    164ms
    -  6   -7.926865075733       -7.37       -3.69    1.5    179ms
    -  7   -7.926865091747       -7.80       -4.41    1.1    150ms

    Obtaining an AbstractSystem from DFTK data

    At any point we can also get back the DFTK model as an AtomsBase-compatible AbstractSystem:

    second_system = atomic_system(model)
    FlexibleSystem(Si₂, periodic = TTT):
    +  1   -7.921706780303                   -0.69    5.8
    +  2   -7.926170425840       -2.35       -1.22    1.0    316ms
    +  3   -7.926840226928       -3.17       -2.37    1.8    357ms
    +  4   -7.926864863222       -4.61       -3.00    2.6    391ms
    +  5   -7.926865036196       -6.76       -3.31    2.0    395ms
    +  6   -7.926865075565       -7.40       -3.68    1.5    312ms
    +  7   -7.926865091643       -7.79       -4.37    1.1    292ms

    Obtaining an AbstractSystem from DFTK data

    At any point we can also get back the DFTK model as an AtomsBase-compatible AbstractSystem:

    second_system = atomic_system(model)
    FlexibleSystem(Si₂, periodic = TTT):
         bounding_box      : [       0     5.13     5.13;
                                  5.13        0     5.13;
                                  5.13     5.13        0]u"a₀"
    @@ -132,4 +132,4 @@
                            
                            
                            
    -
    + diff --git a/dev/examples/cohen_bergstresser.ipynb b/dev/examples/cohen_bergstresser.ipynb index b26c433994..c3768844f6 100644 --- a/dev/examples/cohen_bergstresser.ipynb +++ b/dev/examples/cohen_bergstresser.ipynb @@ -65,7 +65,7 @@ { "output_type": "execute_result", "data": { - "text/plain": "0.4017310500213929" + "text/plain": "0.4017310500217783" }, "metadata": {}, "execution_count": 3 @@ -99,7 +99,7 @@ "└ @ DFTK ~/work/DFTK.jl/DFTK.jl/src/external/atomsbase.jl:94\n", "Computing bands along kpath:\n", " Γ -> X -> U and K -> Γ -> L -> W -> X\n", - "\rDiagonalising Hamiltonian kblocks: 11%|█▊ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 25%|████ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 36%|█████▊ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 54%|████████▋ | ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 71%|███████████▍ | ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 89%|██████████████▎ | ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 100%|████████████████| Time: 0:00:00\u001b[K\n" + "\rDiagonalising Hamiltonian kblocks: 11%|█▊ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 25%|████ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 39%|██████▎ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 54%|████████▋ | ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 68%|██████████▉ | ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 82%|█████████████▏ | ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 96%|███████████████▍| ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 100%|████████████████| Time: 0:00:00\u001b[K\n" ] }, { @@ -111,429 +111,429 @@ "\n", "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - 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+ - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/examples/cohen_bergstresser/index.html b/dev/examples/cohen_bergstresser/index.html index b04c6f5213..7c90cccf1a 100644 --- a/dev/examples/cohen_bergstresser/index.html +++ b/dev/examples/cohen_bergstresser/index.html @@ -7,8 +7,8 @@ lattice = Si.lattice_constant / 2 .* [[0 1 1.]; [1 0 1.]; [1 1 0.]];

    Next we build the rather simple model and discretize it with moderate Ecut:

    model = Model(lattice, atoms, positions; terms=[Kinetic(), AtomicLocal()])
     basis = PlaneWaveBasis(model, Ecut=10.0, kgrid=(2, 2, 2));

    We diagonalise at the Gamma point to find a Fermi level …

    ham = Hamiltonian(basis)
     eigres = diagonalize_all_kblocks(DFTK.lobpcg_hyper, ham, 6)
    -εF = DFTK.compute_occupation(basis, eigres.λ).εF
    0.40173105002168874

    … and compute and plot 8 bands:

    using Plots
    +εF = DFTK.compute_occupation(basis, eigres.λ).εF
    0.401731050021579

    … and compute and plot 8 bands:

    using Plots
     using Unitful
     
     p = plot_bandstructure(basis; n_bands=8, εF, kline_density=10, unit=u"eV")
    -ylims!(p, (-5, 6))
    Example block output
    +ylims!(p, (-5, 6))Example block output
    diff --git a/dev/examples/collinear_magnetism.ipynb b/dev/examples/collinear_magnetism.ipynb index b7df17167b..7174e689eb 100644 --- a/dev/examples/collinear_magnetism.ipynb +++ b/dev/examples/collinear_magnetism.ipynb @@ -48,13 +48,13 @@ "text": [ "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -16.65002922221 -0.48 5.2 \n", - " 2 -16.65070168462 -3.17 -1.01 1.0 18.0ms\n", - " 3 -16.65081053095 -3.96 -2.32 1.8 19.6ms\n", - " 4 -16.65082418022 -4.86 -2.82 2.5 38.4ms\n", - " 5 -16.65082461849 -6.36 -3.27 1.8 20.0ms\n", - " 6 -16.65082469016 -7.14 -3.78 2.0 21.5ms\n", - " 7 -16.65082469750 -8.13 -4.28 2.0 34.0ms\n" + " 1 -16.65004603869 -0.48 5.2 \n", + " 2 -16.65071312796 -3.18 -1.01 1.0 35.6ms\n", + " 3 -16.65081067951 -4.01 -2.33 1.2 38.4ms\n", + " 4 -16.65082419719 -4.87 -2.83 2.2 47.3ms\n", + " 5 -16.65082460834 -6.39 -3.27 1.8 75.2ms\n", + " 6 -16.65082468729 -7.10 -3.77 2.0 47.6ms\n", + " 7 -16.65082469735 -8.00 -4.28 2.0 45.9ms\n" ] } ], @@ -75,7 +75,7 @@ { "output_type": "execute_result", "data": { - "text/plain": "Energy breakdown (in Ha):\n Kinetic 15.9207952\n AtomicLocal -5.0692971\n AtomicNonlocal -5.2202028\n Ewald -21.4723040\n PspCorrection 1.8758831 \n Hartree 0.7793363 \n Xc -3.4467475\n Entropy -0.0182879\n\n total -16.650824697502" + "text/plain": "Energy breakdown (in Ha):\n Kinetic 15.9207407\n AtomicLocal -5.0692667\n AtomicNonlocal -5.2201726\n Ewald -21.4723040\n PspCorrection 1.8758831 \n Hartree 0.7793274 \n Xc -3.4467447\n Entropy -0.0182878\n\n total -16.650824697348" }, "metadata": {}, "execution_count": 3 @@ -145,18 +145,18 @@ "text": [ "n Energy log10(ΔE) log10(Δρ) Magnet Diag Δtime\n", "--- --------------- --------- --------- ------ ---- ------\n", - " 1 -16.66159249546 -0.51 2.618 4.9 \n", - " 2 -16.66811267369 -2.19 -1.09 2.445 1.4 36.7ms\n", - " 3 -16.66904555501 -3.03 -2.06 2.338 2.2 58.0ms\n", - " 4 -16.66909983053 -4.27 -2.73 2.303 2.1 41.9ms\n", - " 5 -16.66910291758 -5.51 -2.96 2.296 1.8 51.7ms\n", - " 6 -16.66910414520 -5.91 -3.47 2.286 1.8 51.0ms\n", - " 7 -16.66910416970 -7.61 -3.78 2.286 1.5 38.3ms\n", - " 8 -16.66910417404 -8.36 -4.28 2.285 2.0 44.9ms\n", - " 9 -16.66910417506 -8.99 -4.97 2.286 1.6 40.3ms\n", - " 10 -16.66910417507 -11.37 -5.27 2.286 2.0 46.8ms\n", - " 11 -16.66910417509 -10.71 -5.77 2.286 1.6 70.3ms\n", - " 12 -16.66910417508 + -11.21 -6.14 2.286 2.0 83.4ms\n" + " 1 -16.66158212275 -0.51 2.618 5.1 \n", + " 2 -16.66811328546 -2.19 -1.09 2.445 1.4 74.1ms\n", + " 3 -16.66904262588 -3.03 -2.05 2.337 2.1 121ms\n", + " 4 -16.66910044654 -4.24 -2.75 2.302 2.0 84.6ms\n", + " 5 -16.66910322305 -5.56 -3.00 2.294 1.9 112ms\n", + " 6 -16.66910415316 -6.03 -3.50 2.286 1.9 84.9ms\n", + " 7 -16.66910417144 -7.74 -3.76 2.286 1.8 106ms\n", + " 8 -16.66910417427 -8.55 -4.29 2.285 1.8 79.3ms\n", + " 9 -16.66910417500 -9.13 -4.81 2.286 1.8 93.9ms\n", + " 10 -16.66910417507 -10.17 -5.19 2.286 1.9 88.2ms\n", + " 11 -16.66910417509 -10.74 -5.83 2.286 1.8 129ms\n", + " 12 -16.66910417508 + -11.24 -6.18 2.286 2.1 177ms\n" ] } ], @@ -175,7 +175,7 @@ { "output_type": "execute_result", "data": { - "text/plain": "Energy breakdown (in Ha):\n Kinetic 16.2947198\n AtomicLocal -5.2227265\n AtomicNonlocal -5.4100283\n Ewald -21.4723040\n PspCorrection 1.8758831 \n Hartree 0.8191963 \n Xc -3.5406834\n Entropy -0.0131612\n\n total -16.669104175082" + "text/plain": "Energy breakdown (in Ha):\n Kinetic 16.2947193\n AtomicLocal -5.2227261\n AtomicNonlocal -5.4100280\n Ewald -21.4723040\n PspCorrection 1.8758831 \n Hartree 0.8191962 \n Xc -3.5406834\n Entropy -0.0131612\n\n total -16.669104175083" }, "metadata": {}, "execution_count": 6 @@ -208,9 +208,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "No magnetization: -16.650824697502035\n", - "Magnetic case: -16.669104175081756\n", - "Difference: -0.01827947757972126\n" + "No magnetization: -16.650824697348167\n", + "Magnetic case: -16.6691041750828\n", + "Difference: -0.018279477734633787\n" ] } ], @@ -249,8 +249,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "(scfres.occupation[iup])[1:7] = [1.0, 0.999998781438549, 0.999998781438549, 0.999998781438549, 0.9582253020763817, 0.9582253020763813, 1.1267050552982909e-29]\n", - "(scfres.occupation[idown])[1:7] = [1.0, 0.8438950511955835, 0.8438950511955791, 0.8438950511955835, 8.140970238349339e-6, 8.140970238348341e-6, 1.2791884254554693e-32]\n" + "(scfres.occupation[iup])[1:7] = [1.0, 0.9999987814393877, 0.9999987814393877, 0.9999987814393877, 0.9582253086018988, 0.9582253086018971, 1.126716187251611e-29]\n", + "(scfres.occupation[idown])[1:7] = [1.0, 0.8438946206715846, 0.8438946206715701, 0.8438946206715584, 8.140935268109707e-6, 8.140935268108983e-6, 9.549341213154275e-33]\n" ] } ], @@ -277,8 +277,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "(scfres.eigenvalues[iup])[1:7] = [-0.06935855813597937, 0.35688560007693537, 0.3568856000769356, 0.35688560007693576, 0.46173606236148057, 0.4617360623614807, 1.1596206852649713]\n", - "(scfres.eigenvalues[idown])[1:7] = [-0.03125745783314898, 0.47618898712528357, 0.4761889871252839, 0.47618898712528357, 0.610249913728152, 0.6102499137281532, 1.22742895464247]\n" + "(scfres.eigenvalues[iup])[1:7] = [-0.06935854892119597, 0.35688560699757654, 0.35688560699757715, 0.356885606997577, 0.4617360745331535, 0.46173607453315396, 1.1596206002663536]\n", + "(scfres.eigenvalues[idown])[1:7] = [-0.0312574722740262, 0.476189033607862, 0.47618903360786313, 0.476189033607864, 0.6102499704863034, 0.6102499704863042, 1.2303523560245693]\n" ] } ], @@ -315,118 +315,118 @@ "output_type": "execute_result", "data": { "text/plain": "Plot{Plots.GRBackend() n=3}", - "image/png": 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+/HhKSoqbm9umTZtWrFhx8eJFmUzW3t4uk8l4PN7t27czMjIyMzMtLS1nz569bdu2V155hbGvszoUhISwqbRUTRC6u0MmY3OynXk5fRonThjvdnw+xo/HoEF9nOLg4CCVSjs7O5uamhwcHHq/Kt9TYufOnUeOHDl8+PCnn3764osv7tixY+7cuYpnHjly5Pvvv5fPh161apV8lMqZM2dCQkISEhIAREZGjhs37vjx4ytXrhw/fvyff/7p7u4+btw4AImJiTdv3pwwYULXtONnn30WgJOTU2RkZEFBgWIQOjk5SSSSt99+e+HChTExMY531xRfvHixvFP0L3/5y9q1a2tray0tuwXQihUr5HO1J02alJ+fr/1XU3cUhISwqaxM5SRCuSFDIDVqL5F5W7UKyvr6WFRQUDBkyBBbW1t/f/89e/YoviQSicrLy+W7QPB4vFmzZsmbem+99dabb77ZIwgbGhq6MqlrRbSGhgbF1dGcnJzkfZJTp049ceKEu7v71KlTAZw4ceLmzZvyf8vv1RXJvdfUHjhw4Pnz57ds2bJ8+fKWlpauxXG67m5raysQCBoaGno8C5TvViG/Zmdnp05fLR3RM0JC2KS2a3TwYOoaNV+NjY2ffPLJ4sWLAUybNq2kpOSPP/7oevWrr74aNGjQhAkTegy8DA8P772Ml7+/f0ZGhvzf6enpXQezsrLEYjEAmUyWnp4uXxpt6tSpx44dO3HixNSpU6dPn378+PGTJ092BaFaQUFBmzZtys/Pf/LJJ7uGp3bdvbCwUCqV9t46mEXUIiSETWVlaoJwyBDIcoxVDeEAsVi8ceNGsVhcUVGxZ8+e0aNHy/d8GD58+Mcff7xw4cK1a9eGhoaeP3/+iy+++PnnnwUCwfXr12fNmvX4448HBgZeu3bt/ffff/zxx3tcds2aNa+99ppAIGhpafnmm2+6OiE9PT1XrFjx2GOP7dmzh8/nP/DAAwDCwsLEYnFtba181/uGhobW1tawsDBN6s/MzNyxY8fEiROtra0TExO7FkU7ffr0v/71r/Dw8P/7v/974YUXrK2t29raGPy66YOCkBA2lZXB27uvE6hFaFbc3d1feeWV+vp6W1vb8PDwZ599VvHx27PPPhsVFbVz586dO3f6+fmlpKQEBgYCcHNzW79+/ZkzZ5KTk52dnf/973/Pmzevx5UXLVpkY2Ozd+/ewYMH79ixQ75ZroWFxfHjxz/99NPt27cHBAScO3dO/hSQx+N98MEHXTMfNmzYIF9vGYCtre3atWu7Lrtw4cIei7V6eno6Ojru3LkTwNKlS5cvXy4/Lt/+9/vvv3/sscdWr14NQCAQ/O1vf5Nf9oknnvC++5MQExMzbNgwpr6kmqBFtzVCi26bCtNadPvmTYSGqlk75uZNuP/b9S8TltCi24yjRbeN5qmnngoNDdVnICgtuk1I/6S2OQjAxQUyGTUKCTEg6holhDVXr8LLS805fD4sLNDebpSCCDGMbdu2sV1CXygICWGNJi1CADweODOqgLCgoaGhurq660MXF5euRWQIIygICWFNWRmCg9WfxuOhvZ3m1JuvXbt2rVmzxu/uhNPHH3/89ddfZ7ekfoaCkBDWlJbi/vvVn2ZhQS1C89LY2FhbW+vk5NQ12z0iIuLcuXPsVtWP0WAZQlhDXaOkt7ffftvPz2/JkiXR0dHffvst2+WYBWoREsIOmUyjwTKgFqERfXLpk+yabKPdjs/jvz7udW9H764jMpls06ZNubm5Pj4+ACQSifx4YWHhsmXL5P9+7733vDT5viEaoyAkhB03b8LeHr1WUVaCx0M7BaFRHCo8dLnqstFuJ7AULB65WDEIeTzeCy+88OabbyYkJNx3330BAQHy405OTjNnzpT/W9UWgERnFISEsEPtvhNdLCxk1CI0jiOPH2G3AKlUev369fb29tu3bysuZu3q6vroo4+yWFj/RkFICDs0fECIO6NGDVsM4YjCwsJjx45du3aN3TV3zA0FISHs0DwIaUK9+fDy8rKxsXnzzTcnT54sbxRSQ9AIaNQoIezQvGuUWoTmQyAQXLhwYcCAAbt3705MTPTw8AAQGxsrX6WaGAi1CAlhR1kZHnpIozNp+oRZ8fDw+Nvf/qZ4JDIyMjIykq16zAG1CAlhh1YtQpmMspAQQ6EgJIQFUikqKjBihKbn29qittaQBRFixigICWFBdTWcnWFrq+n5AgEFISGGQkFICAtKSuDrq8X5NjaoqzNYNYSYNwpCQlhQUqLpA0I5ahESYjgUhISwoKxM6yCkFiEhBkLTJwhhQXExpk3T4nxqERrIzz//TNsbmYSqqirDXZyCkBAWlJTg7jarGqEWoXJSKRoa4Oys27tXrFiRl5d37do1psoRi8WWlv3tlypHPqkJEyYEa7KNtU7Y//QIMUPaPiO0saEWYS/XruH++1FYiJkzsWMHrK21vcDLL7/MbEW3b98eMGAAs9dkXb/8pHqgZ4SEGFtLC5qa4OGhxVuoRdiTTIYlSzBvHurr0dmJdevYLoiYMApCQoytuBg+PtBqdwEbG1l9vcEKMkV796K5GevWwcoK33yDbdtQXs52TcRUURASYmxFRfD31+4tNI+wp48+wttvw8ICAAYPxvLl+PxztmsipoqCkBBj0yEIBQJQi/CerCxUVGDOnHtHVq/GDz9ALGavJmLCKAgJMbbiYu2GjIJahD3s2IElS8Dn3zvi7w9vb5w8yV5NxIRREBJibAUFCAjQ7i1WVpBKaVfCu/bswaJFPQ8+9BB++42NaojJoyAkxNgKCiAUav0uJydqFAIA8vPR2oqoqJ7HH3gAhw+zURAxeRSEhBhVSwsaGjBsmNZvdHamx4QAgCNHMHOmkkG3ISEQi1FQwEZNxLRREBJiVIWF8PO7M9pRK05OFIQAgKNHcd99yl+aOpUeExIdUBASYlR5eQgM1OWNFIQAIBbj3DlMmqT81YkTcfq0Uesh/QIFISFGlZ9PQaiHlBR4ecHFRfmrCQlITDRuQaQ/oCAkxKgKCigI9XD2LCZMUPmqvz86O1FRYcSCSH9AQUiIUeXkICRElzdSEAJAYiISEvo6YcwYXLxorGpIP0FBSIjxSKUoLKQWoR4uXsS4cX2dEBuLpCRjVUP6CQpCQoynrAyurnBw0OW9FIQoKYGVlZqpJzExFIREWxSEhBhPVhZCQ3V8LwUhLl7E2LFqzomKQmoqZDKjFET6CQpCQownMxNhYTq+19ERDQ2MVmNykpIQE6PmHBcXODqipMQoBZF+goKQEOPJyaEWoR6SkzF6tPrTIiORlmb4akj/QUFIiPFcuYLwcB3fa+4tQokE6elKlhjtLTwcGRmGL4j0HxSEhBhJWxtKSxEUpOPbzb1FmJcHDw8MGqT+zFGjkJ5u+IJI/0FBSIiRZGdDKIS1tY5vt7eHWIyODkZrMiEpKRo1BwGMGoXMTANXQ/oVCkJCjCQ5GdHRel1h0CAz7h1NTdU0CP38UFOD27cNXBDpPzQNws7OztTU1MOHD1++fFkqlSq+VFRUdPjw4bKyMuarI6Qf0fw3uSpm3TualobISI3OtLBAYCBycgxcEOk/NA3C5cuXr1y58ssvv1y+fHlERMStW7fkxz/55JNx48Zt2bIlNjZ269atBquTEJOXkqJvi9B8x8vIZEhPR0SEpuePHInsbEMWRPoVSw3P++GHHywtLQFIpdKEhIStW7euXbu2sbHxrbfeOnfuXHh4+Llz5x588MFHH33U1tbWkAUTYpLa2pCfr2mTRhXzbREWF8PRUeWmE70FByM315AFkX5F0xahPAUBWFhYCAQCgUAA4I8//vDx8QkPDwcwbty4gQMHnjp1yjB1EmLaUlMRHAwbG70uMmgQGhsZKsi0XLmiRXMQQEgIdY0SzWnaIgTw559//vjjj9nZ2cHBwStXrgRQWVnp5eXVdcKIESMqVG+AIpFIdu/e3fVhUFDQyJEjdaqZBVKplMfj9Xg4aur66ycl/7zYLqSnxERefDykUh2X/pLJZFKp1NGRV1en+0U4Rav/KV5aGsLDZZp/rwYGWuTmGv97W/5JGfmmhmbqn5SFhfr2nhZBOHz48GnTpg0ZMuSHH37IysqKiYkRiURdLUUA1tbWIpFI6XtlMplEItm1a1fXkWnTpvn7+2t+d3aJRKL+lxnt7e18Pl8ikbBdCJPa29ulUimfz2e7kJ4SE20WLRK3t+vy1Zb/+LS3tzs4WN26hfb2TsbLMz75t5+GQWiTmip+4glJe7umVx861O7Gjfa6OtjZ6V6i9kQikZWVlTHvaASm/klZW1sr5pRSWgRhQEBAQEDAkiVLeDzehx9++Ouvvw4dOrRr1AyAmpqaoUOHKn0vj8eztrb+9ddfNb8dp8h/Yq11ngLGSRYWFnw+36S/xXuT/zdxLQilUly4gK++4uv2a5nH41laWtrZ2bm5oaEBdnb94b9MKpXa2dlp2nbPyuLHxmqXar6+dlVVui/koxOJRGJn3Og1gn75SfWgyzxCsVgs/+0ZFxeXmpp6+/ZtADU1NXl5eWPGjGG4QEJMX0YG3Nzg4aHvdcx01GhtLZqb4e2t3buCgpCXZ5B6SL+jaYvw4YcfHjdunIuLS3p6+pdffnno0CEAQUFBM2bMePjhh5944omvvvrq4YcfHj58uCGrJcQkHT+OyZMZuI6ZBmF6OkaNgrbPfQMDkZ9vmIJIf6Npi3DJkiXl5eUnT550cHBITU2dMGGC/PjOnTunTp169OjRuXPn0jxCQpQ6dgzTpzNwHTMNwrQ07YaMygUEoLDQANWQfkjTFuH8+fPnz5/f+7itre1rr73GaEmE9CvNzbhwAT//zMClzDQI09MxaZLW7xIK8fnnzBdD+iNaa5QQwzp+HHFxGDiQgUuZaRBmZOgy5iUwEAUFBqiG9EMUhIQY1p49mDePmUuZYxCKRCgo0GU7Y2dn8PmoqTFATaS/oSAkxIDa23HwIAWhHrKz4e8PgUCX99JjQqIZCkJCDOjQIUREMDBxQk4gAI+HtjZmrmYa0tN1nwsoFFLvKNEEBSEhBvTdd1i6lMkLml2jUNtVRhX5+6OoiNFqSP9EQUiIoVRV4dw5PPwwk9c0uyDUfBvC3vz8UFzMaDWkf6IgJMRQtmzB4sWwt2fymua1AYVMpuOQUTl6Rkg0o8Vao4QQzXV04OuvcfQow5c1rxZhUZF22xD2QF2jRDPUIiTEIHbuxMiRugz775t5BWFqKqKidH+7szMsLKCwMQAhSlEQEsI8mQz//jfWrGH+yubVNZqWhuhova7g54eSEoaqIf0WBSEhzDt6FDweZsxg/srm1SJMSkJMjF5X8PWl8TJELQpCQpi3aRNeeUXr/RI0YUYtQplMryGjcjRwlGiAgpAQhmVmIjcXixcb5OJm1CIsKICTE9zc9LqIry9KSxkqiPRbFISEMOyTT/Dcc7C2NsjFzahFmJyM0aP1vQi1CIkGaPoEIUxqbMSvvxpwa3QzahFevIgxY/S9iK8vDZYhalGLkBAm7diBGTMweLChrm9GQXjhAuLi9L3IsGGoqYFIxERBpN+iICSESd9/jyefNOD1zaVrtKUF+fl6TSKU4/MxbBjKyhgoifRfFISEMKawEOXlmDbNgLcwlxbhxYsID9dx96UeaLwMUYeCkBDG7N6NhQvB5xvwFubSIkxMREICM5fy8aHHhKRvFISEMGbvXixcaNhbODigvR1isWHvwr5TpzBpEjOXohYhUYeCkBBmVFSgvBzjxhn2LjweBg3q772jbW1ISWGyRUhBSPpEQUgIMw4fxn33GbZfVK7/946ePo2oKDg4MHM1CkKiDgUhIcw4cgQzZxrjRv1/vMyRI7jvPsauRkFI1KEgJIQBEglOnTLseNEu/b9FuH8/HniAsau5uEAs7u9fMqIXCkJCGJCWBk9PDBlijHv18xZhWhosLBAWxuQ1qVFI+kRBSAgDTp/G5MlGulc/D8KdO7FoEcPXpCAkfaK1RglhwJkzePxxI92rP+GUK5oAACAASURBVHeNdnbif//D6dMMX9bbmxaXIX2gFiEh+pLJcOEC4uONdLv+HIQ7d2LkSAiFDF+WWoSkTxSEhOirsBB2dvD0NNLt+m3XaEcH3nsPb73F/JWpRUj6REFIiL4uXcLYsca7Xb8NwrffRliYQZ61UhCSPtEzQkL0dfkyYmONd7v+0DXa0WF54ABqauDpieBgAPj6axw6hHPnDHI7CkLSJwpCQvSVlITFi413O5NvEVZVYeZMK2dnXlgYjh9HQQGkUkydivPn4eJikDsOGgRLS9TWGur6xMRREBKil85OZGUhIsJ4dzTtFqFEggUL8MgjbX/9q729PY/HM9J95Y1CCkKiDD0jJEQvOTnw8mJsXUxNmHaLcOtW2NkZZERM32jgKFGNWoSE6CUtDZGRRr2jCbcIJRJs3Ijt22G0hmAXekxIVKMWISF6uXLFqP2iABwd0dQEmcyoN2XGsWNwdTXqENsuFIRENQpCQvSSno7wcKPekc+HrS2am416U2b873944gl2bk1BSFSjICREL5mZxg5CwDT35hWJcPgw5s9n5+4UhEQ1CkJCdFdZCSsrDB5s7Pua5GPCxEQEBxtph47eKAiJahSEhOguK4vh/YI0ZJIDR48eZXK7XW0NGACBADU1rBVAOIyCkBDdZWZi5EgW7muSQXjyJKZOZbMAahQSFSgICdFdTg5CQ1m4r+l1jTY3Iy8Po0ezWQMFIVGBgpAQ3WVlUYtQMxcuICoKNjZs1kBBSFSgIOS6jg58842h1iIm+pDJkJd3Z8loIzPJIGRl+qAiLy9cvcpyDYSTKAi57rnn8OOPWLgQiYlsl0K6Ky+HkxMGDmTh1qbXNWrkHTqUohYhUYGCkNOys3HoEA4cwCefYM0atqsh3eXksNMchCm2CFNSEB3Ncg0UhEQFCkJO27oVTz0FBwcsXIjGRly8yHZBREFuLgWhZqqrIRZjxAiWy6AgJCpQEHLanj149FEA4PHwxBPYvp3tgogCth4QwuS6RtPTjb0wuVIDBsDWFjdvsl0H4RwKQu7KzoaFBUJC7ny4YAF++800l1rup6hFqKmMDIwaxXYRAKhRSJSjIOSuU6cwZcq9D4VCODggI4O9gkh3eXkICmLn1hSEOqIgJMpQEHJXYiISErodmTEDR4+yVA3p7tYtSKUsrDIqZ2JByNZKdL1REBJlKAi56/JlxMd3OzJ5Mk6eZKka0l1eHgIDWbu7KT0jFItRVMRa27kHCkKiDAUhR9XWoq4OAQHdDk6YgAsXIJGwVBNRkJ/P5u92gQA8HtraWCtACyUl8PCArS3bdQAAfHxQWsp2EYRzKAg5KjUVkZHg8boddHGBhwcyM1mqiSjIy4NQyGYBJtM7yuKYot6oRUiUoSDkKFX7nsfF4cIFo1dDemG3RQgKQt34+ODqVRp7TXqgIOQoVas5jx2LS5eMXg3ppaCgZ8e1kZlMEObns/k0tQdbWwwYgBs32K6DcAsFIUepGmcXG4vLl41eDemusxNXr8Lfn80aTGa8DLvDinqjx4SkFwpCLpLJVPa8hYaishJNTUaviSgoKYGnJ8t7CplMi7CggOWnqT3QY0LSCwUhF1VUwNFR+bYGfD7CwpCaavSaiIKCAvYbOU5OqK9nuQb1amshkcDNje06FFCLkPRCQchFfT9ViY5GSooRqyG95Oez38gxja5RrjUHQUFIlKAg5KL8/L4GYkRHU4uQZVz49W4aLcLiYpYfpfZGQUh6oSDkosLCvoIwKoqCkGVc6Bo1jRZhUREFIeE+CkIuKirqKwiDg1FRgeZmIxZEumN97gRMqEXo58d2Ed15eaGqCmIx23UQDqEg5KK+/4y2tERICG1DwZqmJjQ1YdgwlsswjVGjJSWcC0IrKwwZgspKtusgHEJByDlSKcrL4ePT1zmRkUhLM1ZBpDt5c7DH6nfGZzJB6OvLdhG9UO8o6Y6CkHOqquDiAoGgr3MoCFnEhQeEMIkgbG1FYyOGDmW7jl4oCEl3FISco8nf0BERuHLFKNWQXrgwdwIm8YywpAQ+Puy3nXujICTdURByTmmpmn5RAGFhyM2l5/3s4MjamY6OnB81qsm3Mit8fVFczHYRhEMoCDmnrAze3mrOsbfHiBHIzTVGPaQHjgShtTUsLdHSwnYdfeByEFKLkCigIOScq1fh5aX+NHpMyAqZDIWFnAhCcL93VJO/6Vjh44OSEraLIByiRRBWVVVdvHixoqKix/EbN26cP3/+1q1bjBZmvjT87UGPCVlRWQlHRwwYwHYdALg/XoazLUJ3d7S04PZttusgXKFpEE6ePDkyMvLVV1+NjIxcunSpRCKRH9+2bVtISMi6deuCgoJ27dplsDrNiOYtQlpfxvg4tacQ11uEGn4rGx+PB29v6h0lXTQNwrfeeuvatWuJiYn5+fnHjx//7bffADQ3N7/88ssHDhw4ceLEzp07X3zxRZFIZMhq+z+JBNXVGD5c/ZlRUUhLo622jS0vj+WN6RVxvUXI2a5RAL6+1DtKumgahNOnT+fz+QBcXFx8fX1ramoA/PHHH56enmPHjpWfYGNjc/r0acPVag6uX4ezM6yt1Z/p4gInJxr7ZmwcGSkjx+kWYVMTOjvh4sJ2HSpQEBIFltq+ITk5OTs7e9asWQAqKip8FJ4BeHt7l5eXq3qjVCo9fvx414e+vr6+HFxygm3l5RgxQtOTo6KQksK5NY37t7w8zJ3LdhF3cbpFyNl+UTlfXxQWsl0E4QrtgrCysnLRokWbNm3y8vIC0Nraaq3QeBEIBK2trUrfKJPJxGLxP/7xj64jc+fOXbFihU41s0AkEvF4PGtNWmr6yc+39PS0bG5u1+TksDDrCxcwe3aHbvdqb2/n8/lWVla6vZ2b2trarK2t5b0XhpCdbT9iRGtzs1G7pGUyWWdnZ3Ovddbt7a1v3EBzs47fAAZlmZ9vNWxYm+q14VtaWmQyGY+l6faWHh5WBw/2UZ5uWlpa2PqMDMfUPymBQGBpqSbptAjCa9euTZ069bnnnnv66aflR9zd3evq6rpOuHXrlru7u9L3ylPkzz//1Px2nGJlZWWcIKythbc3HBwcNDl57Fj8619wcNCxKktLy/4XhHw+33BB2NiIlhYIhfZG/rXA4/GsrKx6f1cMGYKyMt2/AQzr5k34+vb9nWxvb8/ab9jQUJSVafiDpjmZTMb4NVnXLz+pHjR9RlhTUzN9+vRly5a9+uqrXQdjYmJSUlLkrcCGhoacnJzRo0cbpEyzUV6u0UgZudhYpKZCKjVkQURBTg6Cgzm0ZBjXu0Y17+U3Ph8flJfj7uh3YuY0DcL77ruvo6PD0tJy48aNGzduPHPmDICwsLD4+Pjly5cfOXJk6dKls2fPpsd+eqqq0mJ/H2dnuLigoMCQBREFubkIDma7CAWcHiyj1eNu4xMI4OpKmzEROU2DcPbs2fPnz6+/q62tTX78l19+EQqF33zzTXR09A8//GCwOs1FZaV2G93FxuLiRYNVQ7rLyUFICNtFKOB6EHJ5sAwAPz8adU3kNH1G+N577yk9PnDgwPfff5+5esxdRYUWXaMAxo7FxYt44glD1UMUZWdj8mS2i1Dg6MjtINTqW9n4/P1RXIwpU9iug7CP1hrlELEYt25BxXgj5eLicP68wQrqLSkJ//0vqqqMeEsOyc5GaCjbRShwcuLqM0KxGDU18PBgu44++fmhqIjtIggnUBByyLVrGDwYWg14jIxEWZmxmgW//465c5GRgZgYM/wN0tiIhgZu9fZxt2u0qgqDB0PdmHWWUdcouYuCkEO0fUAIwNISY8bg3DnDFKSouRmrV+PXX7F1K956C8uXm9vybllZCAnh0JBRAA4O6OwEF5c1rKjg9EgZOX9/M/x7jihFQcghOgQhgEmTcOoU88X0tGULJkzA2LEA8Je/QCTCvn2GvyuHpKcjPJztInrh6AwKbZ91s4JahOQuCkIOqa7W5anKlCk4ccIA1fTw9dd44YU7/+bx8NZb2LTJ8HflkKwsjBzJdhG9ODtDYU0LzjCJIBw0CLa2uH6d7ToI+ygIOUS3FmFsLCoqcO2aAQrqcuUKRCLEx987Mncurl5FVpYh78otV65wsUXI0ceEJhGEoEYhuYOCkEN0axHy+Zg6FUeOGKCgLvv2Yf78bs/H+HwsWwazmTkqlSIrC6NGsV1HLxSEeqHHhAQABSGnVFXB01OXN86di717ma5G0aFDmD2758ElS7B7t5kMmSkqgqsrHB3ZrqMXJyeudo3q0LlhfBSEBAAFIadUV+sYhHPm4OxZNYMmKitx+7ZOZTU2Ije3W7+o3MiRsLVFSopOFzUxqamIjGS7CGWcnTnZIqyspBYhMSEUhBxSVaXjFOQBAzBjBn76Sfmr6emIicHo0fDywscfa3/1s2cRF6d8s+C5c/H779pf0fSkpCA6mu0ilOFi12h7O5qaMHgw23VogIKQAKAg5I6GBlhZwd5ex7evXInNm5X0Ux47hhkz8MILuHYNV67g88+xa5eWlz53DgkJyl+aPRuHDulQrclJSQE3N1bh4qhR+R90nJpxqYq/P23PS0BByB26jZTpMmUKrK2xZ0+3g8eO4fHHsWcPli0Dj4cRI/DTT3j5ZWi3Hen583emD/Y2dixKSnDjhs5lmwSpFCkpiIlhuw5luPiM0FRGygBwcQGfj5oatusgLKMg5Ao9g5DHwz//iTVrUFt758i+fXdScNy4e6dFR2PiRHz5pcbXlUiQloYxY5S/ammJiRNhsvstaygnB+7ucHJiuw5luPiMULdpQGyhRiGhIOQOPYMQwJQpWL4cEyfim2+wYgVeeAEHD3ZLQbk1a/DFFxpv55ubC09PDByo8oRp04wyn59NFy4gLo7tIlTgYteoaQVhQAA9JiQUhFyhfxAC2LAB//d/SE6Gnx8yMpQ/1oqJgYMDEhM1u2JysppRIpMn4+RJHUo1IefPKxkzyxFcHCxjWkFILUJCQcgd164xs2vNggX46iu89VZfk97kMwA1cuWKmnkDwcFoaUFFhVZFmpbERJWjhVhHLUJ9BQRQEBIKQq5gpEWooYcewv79mk2FV7vUNI+H8eNx5gxDpanQ2YmjR1lZFrKqCo2N3NqYXpG8RcitVQ0oCImpoSDkiupqDB1qpHsFBsLaGjk5Ggxw12TPhQkTcPYsI4UpJ5Vi3jy88Qaio1FebsAbKfPnn5g4kbtzAaytIRDoulSCgZhcENIzQrNHQcgVxmwRApgxAydOqNsCuKoKNjZwc1NzWny8xo8cdbJ9O+rrkZSE557DK68Y8EbKHDuGadOMfE/tcKt3tKMDDQ2mMZtezskJNja0B4WZoyDkBJkM168br0UIYOpUnD6t7n9fw52HwsNRXm6oMRsyGf75T7z/Pvh8vPwyzpxBSYlBbqSMVHpnRQIuc3a+N2eGfVVVGDoUFib1i4V6R82eSX2/9l91dbC3h0BgvDuOH4/z53kSSZ8nZWcjNFT9tSwtERODS5cYKq27ixchlWLyZACws8OSJfjf/wxyI2VSUuDsDB8fo91QFy4uXGoR6rxyPIsCAlBQwHYRhE0UhJxgzAeEcm5ucHdHdnafz75ycjQdJRIXhwsXGCmsp127sGTJvQ8XLeq5fI4h/fYb5s412t10xK2uUdN6QCgnFFKL0MxREHICU3MntDJmjPTChT6DMDcXwcEaXWvsWFy8yEhVPR040C2LxozBtWtGm63xyy+YP984t9Idt7pGTTEIqUVo9igIOcH4LUIAY8ZIL11iKAhjY5GUxPwo/oICdHR02w+Xz8eUKcZZ1C05GVIpR9faVsStrlETDUJqEZo3CkJOYKVFOHq0NDlZdRDeuAELC7i6anStwYPh5MT8n9UnTmDq1J4HjbWWzbffYtkyI9xHX9Qi1FdAAEpKNF51kPRDFIScwEqLMCREVlXFa2pS8XJ+PgIDtbhcTAySkpioS8GpU5gypefBhAScO8fwjXq5fRu7duHJJw19Hwa4uHApCE1xsIy9PZyc+vfqSKRvFISccO0aC0HI52PkSFlamoqXuRCEShc3Cw7GrVuG3jpn2zZMnWoabRtuBaEptggBCIX0mNCcURByAitBCCAiQnUQFhRAKNTiWowHYWkpACVzFywsDBK6CkQifPQRXnvNcHdgEoeCUCLBzZvsfCvriYLQvFEQcgIrzwjRdxAWFmoXhFFRyMyEWMxEXQCAixdVbgg8ejRSUhi7US9ffIGICBMYJiPHocEy16/D1RWWlmzXoT0KQvNGQcg+mYy1FmF4uCw9XcVrhYUICNDiWg4OGDEC2dlM1AUAuHRJ5YbA0dFITmbsRt3V1eGDD/DBBwa6PPM41CI00X5RUBCaOwpC9tXXw9bWqMvKdAkJkRUWoqOj1wsSCUpK4O+v3eWYzaekJMTGKn8pIgJXrjB2o+7efhuLFmm0og5HODmhqQlqFgkyDgpCYpooCNnHVr8oAIEA3t7Iy+v1QmUlXF1ha6vd5aKjGeuxFIuRkYGoKOWv+vqiocEQq5smJeG33/Dee4xf2IAsLDBoEDd6R01xyKicjw+qqyESsV0HYQcFIftYmTvRJSwMmZm9jhYVad0cBBAVhdRUJooCcnIwfDgGDFD+Ko+HsDBkZDBzr7ukUjz3HDZu7GtPY25ydeVG76jptgitrDBiBIqL2a6DsIOCkH0stggBjByJrKxeR3ULwshIZGais5OBslJTVTYH5ZTXrZdvv4VAgMcfZ/aqxuDiglu32C4CphyEoBVHzRoFIftYbxEqCZTiYvj5aX0t+XiZ3FwGyrpyBRERfZ0wciSTA3OA1lasX4///Ie7e/D2gSstQtPtGgUgFCI/n+0iCDsoCNnHboswNFRZoJSU6BKEYK539MoVREb2dQLTQfjFF4iLM5kpEz1Qi1AfIhEOH0aLp9m3CDMz8fDD+Pe/mV80mPMoCNnHbovQxwc3bqClpftR3VqEYCgIZTKkpyM8vK9zgoORk6Pvje4SifCf/2DdOqauZ2ycaBHKZKiuNrkWYUcHpk7FunVYsVHYkWnGLcLaWsyciXHj8OOP+OwztqsxNgpC9rHbIuTzIRT2GjhaUgJfX10ux0gQlpXBwUHNet9DhkAmY2qhtd27ERrabZcL08KJFmFNDQYMYGcakB4++QSOjkhKQuzjwrZ0M55B8c9/Yu5cvPQSduzAhg24fZvtgoyKgpB97LYIAYSEdH+uV1MDKysdh05GRiIjQ9+F/DMy1DQH5YKDmXkeCXz5JZ5/npErscPNjQNBaIL9op2d+M9/8OGH4PGw6v88LEUtJWmNbBfFhvZ2bN2KtWsBIDAQU6fi++/ZrsmoKAjZd/06y0HYM1B0bg4CcHSEm5u+z1rU9ovKBQYyEoS5ubh6Ffffr/+VWOPqauhFyDVggiNljhyBvz9GjgQAewdevZvwj0/NslH4+++IjoaX150PV6zADz+wWpCxURCyrK4OAoHWM9eZpSQIey91rbnISKhcwFQzGRkadVMGBjIyzG/HDjzyCPh8/a/EGldXahHq4qef8Oij9z60ixAWHzbLINy7Fw8/fO/DKVNQVmZW+1JRELKsuprNB4RyQUHdnxHq0yIEEBmp72NCDYMwKIiRINy9G488ov9l2OTmxoEWoakFoViMI0cwZ869I85xwuFtBQx1t5sOsRh//IEHHrh3hM/HrFk4cIC9moyNgpBlXAhCf3+UlSnMgy8t1SsIo6L0ahG2tqKqSqP1vpmY+JWZiY4OREfreRmWUYtQB5cuwcur+0+fUDjOreDoUdZKYkdSEkaMgLt7t4P33Ydjx1gqiAUUhCzjQhDa2GDYMJSU3P24tJTNrtHsbAQFabSVj68vqqqULRmuhf378eCDJjmJXpGjI1pb9fxK6K2iwrSC8MQJTJ3a/ZBQ6C8tOH6cnXpYc/Ikpk3reXDqVJw6pe+oN9NBQcgyLgQhgMBAhd5RPbtG3d1hY4OrV1W93tbW54Td9HRN5zFYWWHYsDv79+rq4MFufUImisfjwAyKigqMGMFqBdo5cwaTJ3c/JBQOulFw/pzMbH7/AwDOnMGECT0PDhmCwYMZX8WQsygIWcaRILzXyygW49o1DB+u1+VUPyZcvx4uLggKUr3pTVbWnWF8mtBv95yGBmRlKfklYIoGD8bNm+zd3tRm04vFuHwZ8fHdjw4axHOwD3OpNpvf/4BUiosXMW6ckpfGj8fZs0YviB0UhCzjSBDeG3dSUQF3d1hZ6XU5FY8Jf/4Zu3ahvByvvIL581Wszq3hSBm5gAB9pmqcPIn4eNjY6HwBDmF5vExtLQQC2NmxV4F20tPh7a1srmxg4P3+BRcusFASO3JzMWQIXFyUvDR2LC5eNHpB7KAgZBlHgvDeTAQ9HxDKKXtMKBbj9dfxzTdwdcXKlRg2DFu3KntvZibCwjS9kb8/iop0LvPPPzFlis7v5haWg7CyUt9eBOO6fBljxih7QSiMd8k3m9//wKVLKre/joujICRGwpFZyPe6RsvK4O2t7+WULbT266/w8UFCwp0P335b2eq+166Bx+s5gK0P+rUIT53qP0E4eDCrQVhRYXJBGBOj7AWhMJBXcPmysethTVKSii8EEBSEmhpDbH/NQRSEbJJKUVOjxa99w3F3R2cnamsZahF6eUEkwrVrise2bsXq1fc+TEiAjQ3OnOn+Rq36RaFXi7C2FpWVavZ6MiFubqw+IzS1IaMpKSrmzAQGutYVlJX1Woa+v+pj408LC0RGIiXFuAWxg4KQTTdvwslJ3+dxTAkMREEBQ0GIno3CGzeQnIwHH+x2ytKl2Lmz+7syMrToFwXg5YXr1yES6VBgYiLi4kx7QRlF1DWqubY2FBerGJIlFPIKC0aOxJUrxq6KBWIxsrL62u8sOpqCkBhcVRUnHhDK3ekdZaRrFD1/hH77DbNm9dyZYOFC/PZb96lKGi633cXSEsOH6zaD4vz5XoMGTRnLo0ZNqms0IwNBQSoGSfn6oqJi9KgOswjCvDwMHw57e5UnRETou1yintrbsWsXdu5Ec7NB70NByCaOPCCUuzMTobTUEEF44EC3tazkfH3h6orkZIVD2naNAvDzQ3GxDgVeuEBByByTCsK+FnW3tsawYQkeJWYRhOnpap4NRESw2TS+cQOjR+Pbb/HTTwgJwaVLhrsVBSGbODXzSihEWb4IdXXM1BQd3dU1KhLhzBncd5+Ss+6/H4cP3/1AJEJREUJCtLuRv78OQdjZibQ0lcPlTBEFoebU/P4XCiPtC8wiCNV2wAQFoaKC19ZmrIIUyGR47DE89BCOHMG+ffjiCzz4oOH6aSkI2cS1rtHm7KsYNgwWTHxX+PigvR3XrwO4cAHBwXByUnLWtGm4t6JVTg58fbXe2dXPT4fxMpmZ8PbGgAHavo+72AxCqRTV1SY0WEZNv4NQ6NNZkJsLicR4JbFD7SN5S0sEBVnk5BirIAW//IL6eqxff+fD2bPx1Vd46KEeQ/CYQkHIJk51jQYEgFdWKvNmYqSMXHS0vN/zzz97Lep4V0ICrlxBaysAjbch7EGnrlGV08hM1qBB6OwEK3+74+ZNDBpkQgsTZGX1+fs/MNCmLN/dXZ8ZqiZCkzm7o0ZZsLLQzt//jvff7zaYbd48PPMMHnkEYjHjd6MgZBOngtDBAcG2ZS1u3oxdMSYGSUkATp/GpEnKT7GzQ2Qk7izkkZamy2wGnYIwORmjR2t9K45jrVFYXm5C/aIVFbCzU76Uyh1CIQoKwsL6+0KbdXVobla/PGxYmEV2tlEKUnDmDMRiJU9T/vY3DBiA115j/IYUhGzi2sY14QNLr9sy1yKMjcXlyyIRUlP7GpYyfvzd2YRXrvQ1klsVX19cvaptN1a/DEJ3d9y4wcaNy8tNaLlt9WvZCoUoKAgNNY0glEpRVqZTGyknByEhihuvdHairKzXGhcjR/KNv0Pjt9/i6aeVbApjYYHt23HjBuPbYlAQsolTLUIAAVZlJVJvxi4XG4ukpOQkWWBgXyO0x43D+fOAVKp+DJtSAgHc3FBZqfk72tpQWKjdfEWTMGQIBaF62dkIDe3zDE9PNDdH+jYavyGkrcpKREcjPh4BAd1HX2siK0vxC3H2LHx8EB+PuLju30UjRxr7GWF7O/btw5Ilyl91dMSOHcyMY1BAQcia27chkShb9pc9wzpLs1u8GbvckCFwcMjdX6R0afsucXG4fBmS/CI4OfXZY6War69WvaMZGQgMZPSRVlsbvvoKW7agvZ25i2ptyBD54CSjM6kho+qDkMdDQEC4bQErY0Q0J5Fg/nw8/DCqq/Gf/2DOnD62PlMmJ6eraZyfjwUL8O23qK7G9OlYvFihXejhAbHYqIs1HDuGUaOMvOAWBSFruNYvCsCpsSzpFnNdowDi4lr+vDR2bF+nODvDwwPVB1J13ydey8eEqXrcSom2NkyZgkOHsGcPpk9nMQupa1QT8h5BNYRCL1FBSYmKDVK4YcsWDBiAt94CgHnz8NJLWLlSm/fn5CA4GIBMhqeewvr1mD4dADZsgEiEHTvunSgNDoYxW8cHDmDuXOPdDgAFIYs4F4QtLdYdzZevDmHymnFxjrkX4uLUnDVmDBqO6/HUTssgTEvT5VmkSu++Cy8v7NuHgwfh4XHnN5Px1dQMH9REQdg3mQx5eRoEYWCgVUn+iBH6rOhuWBIJNm7EP/5x78grr6C6GgcOaHyJ3Fx5EO7cCbEYzz5757CFBf7+d/zjH/cahUYNQpkMBw9i9mwj3e4uCkLWcC4Iy8rg5VVVzdNp5U7lbvrHjxadU7tSTWwsBBmXdZ/f7ueHkhLNT2cyCKuqsG0bPvkEAHg8bN6M7dv12StYR+vWITDwyfVegZd+MPatAZSXw8uLhftqr7ISDg4YNEjdeUIhCgpCQmD8YSIaOnQIQ4Z0mwJkaYn338e6db1GuyjV2IjmZgwbJpHg3XexcWO3gSlTp4LP43ThQgAAIABJREFUx6lTdz6UBgXBaN3EOTmwtoZQaKTb3UVByBrOrddfWsrz9fHyYnL61Pm2SF+UoKmp79Nio8QeN6/o3l+pzTNCsRg5OVqv46bS5s1YuhSDB9/50MUFL76IjRsZurpmvvsOe/eisDDjm0uPpq/VftSEflpbcfv2va8At91tBakTGIj8/OBg7gbhDz/g6ad7HpwzBzweDh7U4P25uQgKAo+3axc8PJTMblqxAj/c/ZtKEhhovCA8fvxOF61xURCyhnPr9ZeVwdv7zoqjDLmUanVj+Gio2/B7lEVWOUa026j9Q10FbVqE+flq1hnWgliMbdu6bS4F4LnnsHev8QYXNDXhjTfw449wcRkUI/zQaSNeeslIt5aT/0HXe6Q7J2kahEIhCguDg2TcDMLmZhw7hgULeh7n8fD66/jXvzS4RF4egoIAbNqE119X8vrixdi37862LkZtEZ48qXL1DUOiIGQN57pGS0vh43Nvq3omJCdDPHZ8r10He7JOuZDvFJeeruttnJ3B4+HWLU3OvXJFl+VrlDtxAl5eCAzsWcxDD+G77xi6hzqbN2PGDPmn5O6Or1oex61bar/gTLp61VT6RQHk5WkWhAMHYuDAcJdKbgbh4cOIj1c+4HzhQly9qsGSnLm5CA4+fRoiEe6/X8nr7u4YORJ//gkAMvlupZr9fOlFKsXZs5g40eA36oWCkDWcG3NeWgpvbwaDUCZDaipcF0y897RBlcTExtB4vRbU1bhRqO1GT3359VcsXqzk+JNPGikIxWJ8/jlefVX+kYMDpLAQPf0XfPmlMe4uZ1JBKO8R1EhgoL8kv6CA8anbDNi/v+fWnl0sLfH88/jvf9VdIi8PQUEff4wXX1TZmH/wQYWhN8HByMvTrVotZGbCzQ1DGB2vpxkKQtZwMQgZbRGWlMDBAY6zxiIzU812YomJ1lMSFPbx1Z7GA0d1W9BUCakUv/+OefOUvDRuHNrbjbF/zeHDGDFC8YHn0KGonPQ4Dh9W+1yWMVevMrNvl1Hc7RHUQGCgbXm+szMqKgxbkrakUhw9ilmzVJ6wYgUOHlQ3ozQvr9Ih6Nw5LF2q8pRZsxR2hjHO89LERCQkGPwuylAQsqOpCVIpt2bTo7QUvr539qlnQkoKoqIAW1uMHt1Xo7CkBGKx70yhXjuAahOEzIyUSUmBqyt8lE275PGweDF++omJ2/Tpf//DE08oHhg6FFWtTpgwAb//bvC7y5WVmUqLsL4e7e0a7/cSGIiCgqAgYzSEtJKRASenvr7kTk545BF88YXqS3R0oKLi4/1+Tz0FOzuVZ4WGoqPj7tA54wTh+fPoe/UNg6EgZAfnmoN1deDx4OQ0eDBkMmYeB6Sk3B0HOmMGjh1Ted7Jk5g0KSwMeXno6ND1ZpptxlRTg44Ohr7yR45g5kyVrz78MH75hYnbqNbcjKNHewyZ8PBAdTUwfz727jXs3buYTtdoXl7P57l9EQqRn8/BIDxxAlOmqDnnr3/FV1+p3oqkqEg6bMR3P1o9/7ya60ybhhMnABgrCNnbLFu7IGxvb2/u1cfV3Nyck5PTemcrHaIRzk1BLi3tatwEBjLzw5+aiqgoAMCsWTh0SOV5x45h2jQ7O/j46DE2TbMWodr917Rw/DimTVP5amQkZDJkZDB0M2UOHkR8PJydFY8NHYrqamD2bPz5px5/VmijtNRUuka1C8LAQOTnM/WzwKBTpzB5sppzhEKMHav6OXVeXolV4KxZ6gfrTZ6M06cBGCUIr19HU5PxZxDKaRqEP/74Y1hYmIODwyOPPKJ4fPfu3V5eXkuXLvXy8jqgxaoG5u7qVY4FYUlJVxAGBTHwmFA+UuZOEI4ahY4O5T9IEgmOH5fvtxIeDt0HjmoWhJrsv6aR1lakpmL8+L7OmTcPv/3GxM1U2LsX8+f3OObhgWvXAFdXBAUhMdGAd5cTiVBby62V41XLz9f4ASEAHx/cuBHi08bgIGr9SaU4dw4TJqg/c+1a/POfyv8W6szKP3o18I031F9kwoS7QejlhVu30NKiXblauXwZsbFszcPRNAgDAwM3b968bt06xYNtbW2rV6/++eefU1JStm7dumrVqk4ur83HJVxsEfr6yv/JSHfQ1auwtb27cC6Ph3nzsGePkvMSE+HtLX9uExGhx/gST080Nqr9Qc3MZOgB4YULCA9XMxtx3jzs28fEzZTp7MTRo3jggR6H77QIAcyYgaNHDXX3LuXl8PTstnsqh2nXIuTz4esbYlXIqRZhZiaGDNFo9YK4OAQFYcsWJS/l/57PDwlUs/I4AMDHBzweSkstYGEBodCwreNLl1jcLFvTIBw9evT48eMdHBwUD/7xxx+urq5TpkwBMGfOHJlMdsaYE5hMGeeCsKSkKwgZ6QXpuYzZokXKB4/8+mtXs0avIOTx4OOjtlHIWNfo6dPqZzvFx6O83FCDDs+ehVDYe4V+T09UVQEApk+/+3jHkMrKTKVfFEB+vjZBCCAwcHB9/u3baGw0VEnaSkzUYjTJBx/gvfdQV9ft4PXrEGXkz16j6RciIQEXL/IBICjIsL2jSUm6L7KoN70Gy1y9etXf31/+bx6P5+vre1X1RiAymSxFwXV2NozhCs6NMOjeNar/N3xqavcgjI9Ha2vPib5iMXbv7pqKFx6u3zM1db2jEglyc9VtyqohTcZ58/mYNQv79zNxv14OHVI6EfpeEMbGoqDA4L/CFR4tc5x819m7v640ExTEy88TCplcYkJPFy5oEYQREVi8GIojYmQyrFyJEH7+sKmaBmF8PC5d4gMGfkwokyE5GTExhrq+Opb6vLmlpcVGYVc3Ozu727dvKz1TJpN1dHQ888wzXUcWLlz44osv6nN3YxKJRDwez9ramqkLlpXZu7q2Njdrsj6uobS3t/P5fCsrKwB2RUXt7u7S5mYAgwfj+nWHmppmW1vdL56UJHjiCXFz872ds62XLuV98ono88+7jlju22fl7982ZIh8lqGdHWxs7HNzW4cP1+XLYuPlJcvJaU5IsLa25ivrrCsstBg8WAC09j2nUb3OTvukpNbwcJm6C1lOn261fXvbsmX63Q8ymayzs1NxnJrdoUPtX34p7VXAoEGornaQn2k7enTnsWPiPoa26s06Px+enh06fUFbWlpkMhnPWM+ECgsthg4ViMVa/O9bentbnjjh5ydOTxeHhGi0B3xLS4tBP6Pz5+1efrm9uVnTSf5/+xtmzLB75RXxu+92SKV44w0bybVbAls029qqmdp7V3i4xddfWzc3N1v6+Fj+/HO7vj88ylkUFQkGDmwVCDSsSisCgcDSUk3S6RWEgwcPrq+v7/qwrq7OXcVuijwez8bGJlWvKdNssrKyYjAIOztRUwOh0F7d/45hWVpa3glCsRjV1XYhIbj7Cfr7o7LSQZ8tGjIyMHasZbeu9BdeQGCg1Xvv3esU/vRTvP66Yn97RAQKC+01WgSrt+BgXLni4OCgKgiLixEejh7d+7pISYGPj70m89HmzsXzzzvweHqubcrj8aysrO5VXlGBW7fsEhJ679Pt4AAbG3R0ODg7A5Mm8S9fxsKF+txajaoqzJ1rreuX1N7e3mhBWFGB4GAt//cjIvDVV2FzLa9etdTwfTKZjIFvMBVqatDQgKgoO82/Zg4OOHoUS5ZYjxplLRYjIgK7NhTx/h6seZFjx6KsTAY4CCIj8Y9/GOqzy8lBbKzhvnRq6dU1GhUVlZqaKhKJANy+fTs7OzuSyX3e+q3KSri7g90U7KaiAkOGQCHm9ewFuXYNHR29+n5dXPD883j55Tsf/vgjOjp6rMyi18BRf/++pxJmZTH0gPD8efS913CXgQMRE4Pjx5m4q4KjRzF9eu8UlLvXOzp+PM6eZfjWPSj0qHOcdkNG5QIDUVAQKJRxpGv00iXExGg9rNLNDceP48gRnDyJAwfgUJWv1RQFa2uMHClNSQH8/VFWZqitipP12I6UCZoGYWFh4ddff33x4sXy8vKvv/769OnTAKKiosLDw1evXn3+/Plnnnlm8uTJQpZmgZgWzo0wKCmBn5/igZAQvZabT0u7O3GihzfeQHk5Vq/Gli14+WVs3drjt7lBg5CxuRMXL2oahAAeeECbzVI188cf8gknSnl6orISABAbi8xM1dOqmaAwxorjdAnCgQMxYECYUyVHBo5e1mPLzqAgBAQA0GHIEKKjJZcuATY2GD6cyU3aFCUn674LGxM0DcK6urqUlBRnZ+exY8empKR0DYrZs2ePg4PDO++84+HhsWPHDoPV2a9wLgiLi3sEYWioXltS35tB2INAgGPHIBDgyBEcONB7e1y9xsuMGIGbN9Herup1xoJQq3HeDzyAgwc12yxVMxIJ/vyzjz3bhg+/G4R2dggNNeD2hI2N6OgwlZ0Itf/9DwAICvLtzC8pgUTCfEnaYmZYpfZfiNGjpUlJAAw2XkYqxZUrKn5lGImm3XNjxowZo+yH383N7dNPP2W0pP6Pc0PtDBCESndlAABHR3z8sao3CoWorkZzM3R5WMDnw9vborRU6aLara2orr77R7E+bt1Cba0Wv0r8/eHoyOSIuORkeHpi6FBVrw8bdjcIAcTH48IFNRP/dVZcbCrNQWi13LaioCCb0rwhQ6aVlfX4EWFBSgoT3YfazaYEgMhIyYYNAICQEIMEYUEB3Nzg5MT8lTVGa42ygHOLUhUV9fgp9/dHVRV0XjXv3iqjWuLzERyMzEwd74uAAJ6KrpusLAQGMjHzOykJo0erej6n3Jw5TC6BfexYX0u79QjCuDhcvMjYrXvo9fcTZ9XWQiLRqe0aHIzcXC6sOFpWBhubPv7+0UxHByoqtJxEAl9faWMjamqA4GCD7NCr8+8L5nA1CA8cUL5xcr9QVsb1FqGlJYRCHb/na2rQ1KT7b0i9HhOqDkLG+kV1eFAzdy6TswmPHcOMGX28Pny4wiR+CkIAurSC7goKQm4uF1YcZSYsiosxfDi0HP3O4yEqCsnJeo8dUEXloxTj4WoQenr2tUyziePWCAOZDMXFvf9IDAvTsWWWmoroaN2XDDRQEDK2powOnZxxcbh+HaWlDNz99m1cudL3WpPDh6O8/O4H8pG79z5mVFGRtm0LtugyUkbubouQ9YGjzAyrzM+HTpOToqORkgIEBaGwkPmtiqlFqFJYGMrL0dDAdh3Ma21Ffb3Gm6IZwfXrsLXFwIE9DuschHr+xOo1XkYotFAdhMysMpqUpHUQWljgwQeZWYD75EmMGYM+VzoYMQIVFQqjc8aMwaVLDNy6N9MJQh0fEALw9ERb20jPetZbhMnJTLSadP1C3AlCe3u4uTHzJ10XDoyUAXeD0NISUVG4M1apXykthZeXds+YDEtZcxDAqFE6BpKeA6FHjUJmpq5/dAqFPBXbCjOz3HZ5OSwsdNlsYf585WuOa6vvTRABAPb2sLNT2FEyNpaCUPcgBBAUFIxcdoNQvpcLA60mXfuIo6JwZzUUxntHi4rg5AQXFyavqT3u/D7uxXB/ybKKW/2iAAoLlf4607mLUocmkyJHR7i4aLjbfC+enrzmZjQ19ThcVQVLSwwZontVd+jc2p0y5f/bO++AqOv/jz+PjcjeogiCIlsUBLfiSFRcWJrbLNRKrW+aWv6ytCzLnGVmlosUZ6g4ERUVBWWogKIyRAEBRfaGe//+OD0POLi7z71v4efx19378x6vz43P6z1eA/fuvU4MIQWtehDysbUV2A3t3Rs3b0o7bnMqKlBUJDqjnXIglSJ0djbJv19fj8JCmiJJxOPH0NWl8QNm+kE4OKC0FM+fS21Q3hwlOCCEsitCWfyBFY3SWRg8eiQ00oSVFdTVBewPxYMXU0ZKm1jmaSg4HK6jY/NFIbV9UcZeEFpaGDtW2pz1Dx6grg5ipM+xtcWb6Pc+Prh9m74fXFoaunRRpp2NFqmpQXa2FLNPFxfcuyefDO0tQU1ZMD0s5XDg5YWEBBkowhajb8gVJf4dy2gmq2iUbj/p0aOW3Ot69EBiomSd8aJASYk0+ZiIsGQBd+7QU4SMzz8nTxaeiEp8Tp9GQIA4Fe3s8Pjx6zcGBujYkfLDC639bJSNR49gbw9NTabtXVxw756scxC1Dh1l8ewZtLRgYsKs9avdUVdXJCdLLYoA8fGsImyVjh2hpiYrgzfFoXQrwocPW3qieXlJrJBu3qSQXFNKRSh0RSjMyV7SrqU7qBk2DOnpyMhgLsCpUxg9WpyKjRQhAB8f+sftDyQLWalA7t+XYl8UbWhFmJrKzGSUxytF6OKChw9RL1YuDtHw/lOsIhRBW1wUKteKkMtFenpLirBXL0iaLyQmRsGKkCtsRXj7Ng1FmJmJdu2YRxTT0MDkyQgJYdi8thZxca270vNpqghl8T96+JCpa568ke75D3TujJcv3TuXKlYRUkhnIN2MoGdPJCYC7dqhQwdqEUczM6GnR+PwU1qUWxHKYiarUGprkZ2tRN70nJwcGBpCX1/oVW9vyQJV1tcjLo6CIuzcGdXVyM9n0pY0iwJSVYWsLOkehTyk9+SaPRt79jCziOVkZWHgQLRrJ05ldkUoyL170n37amro3t1d/Z4sXMnFIScHXC46dZK6o/v34eLCuHW3bigoQHGxFJ5VzVECD0IerCKUKxkZ6NhR0sAOsqTVeb2dHaqr8eyZuJ0lJcHWFkZGFOTy8pL4eJIH19GR0zhGclISnJykOCLiI32ox549YWiIixeZtE1Px7hxYta1t2/s6+XpiYcPKaehYO6jLm/u3ZPm+Q8AcHW1fplSWCiLrLGiobZ3KN2KUE0NHh5ITATc3akdE7KKUCx4e3MUI/crmhYsNBUG59Gj1je4JNpUi45G374UpAJem6gxgGdmLqAHbt9Gjx40ZKIS2yM4GNu3S9yKEDx9Kr4iNDCAjg4KCl6/19aGiwvDmYVQ8vKgpaVw3y9xaGhAWprUm7hubpx7KU5OijkmpKYIpZ4RvDomdHNjV4TyxdQUpqZowUVaFVG2gxWOqHm9RN7Y0dHo14+CVOAfSDCCNA4NnJhIQxHScmmePh1RURKbgNXVwcICZmbit7C3b2yX07s3zc0Vqfzy5Ep6OqytxdxRbhlXVyQnu7rKJNCmSOgowqIiVFYyiQUhwCtFyDjWRhN4/ylaWVmkQ7kVISQ/p1JulO0BwhGlmf38cOOGuL1dvUot4c+bSBYMaBz8IjGRhqEBrfgX7dtj9mxs3ixRI05traQTKAeHxorQx4emvUyzTbaUFCxfjl9/bR7MQMFQ2BcF4OaGlBQXF/pOKOJAx3eCd1LKOAQwAP701NER+fkUvun0dOjrw9xc2n5ooPSK0MeHVYQyRJRAfn6Ii0NdneieHj2Cmhq1oDldu6KwEC9fMmkruCKsr0dyMo0VIS/7EhU++wx79gjEQBPFixeoqyMS+tw4ODS27KMbaK2xegkLg78/tLWRmIjevSU4VJYDKSniRCAQRceOqKz0si2UvyJ8/hzl5TTM62h8EM7OePIE5VXqcHWlsDtKMUmn1Ci9IvT2bkv2MszTwciC8nLOy5ewtW2liqEhHB3FWpxdvozBg2lJBjU1KY4JBXx+799Hp04tWcVKgpSB4wSxscF77+Hnn8Wtv3cvtLQktfZxdGysCJ2cUFgogfZtHYGnamoq5s3D2bP47juEhGDqVLz3Hv38BIyhowg5HLi5eXCS6LqSi0NCAnr0kHIhBwBISZF+aaypCVdX3LkDeHoy93Dic/MmqwjFpmdP3LlDzX9ToTx/DkKUwWfmFWoPH5Ju3URGyRo0CFFRonuLjIS/Px3BeDCeAhFnZzx4wDMcpZPUG1QVIYCVK/HPP2KdFHK52L6daGtLOkJTRaimBm9varujAupl/nysWvVm83nlSqipYedOOuNIT3Iy3NxodOThYfU86eVLee/9UrMmuXePxowAPXsiPl4Kq25B6P6npEPpFaGBATp1UswhNW2k9WeiDef+fSLGRu3gwbh0SUQdLhcXL4rp7S0uzH1n9PRgbc2L203nOVJfjzt3aJq3deiATz/FkiWia544ARMTaGhIOoKjIx49alxEK4p9fj4aGni50sPDUVSEefPeXFRTw6ZN+O47VFZSGEpK6uqQnk7pMMLdnZN018WFcnwxkVBThJRmBK/yMUmvCOvrcfs2teMGqVF6RYi2Yy9D59yeHpx794gYAg0ejOvXUVPTWp34eFhYUE5FIJUT6WufXzpHe8nJsLWlscEqwPLluH1bROZ6QvD991i+nEH3VlaorW18yOrnR0cRJiXxcxz/8AO++Qbq6o2ue3nB1xf//ENhKCl5+BC2ttDRodGXhwfu3KHoSi4mdLIvPX+OmhopTUZ5eHu/NhxNTUVtLfOOkpJgZ0f5PyUFqqAI24pbfXIylc0Jaqjdvy+OQEZGcHHBtWut1RE7CqYE2NujpoZp5iJ3dyQl1dYiOZmGyejNm+jdW+peGqOjg127MG8ecnJarBMaCjU18d0Hm9CtW+NFIS+di/ReucnJPEV4/ToKCzFhgpAqS5ZgyxbFnxTevctX2VLj7o5799xdGuS5IiwsRHExjYiM1DaI4eqKzExUcHXh4CDVpIBKMEZ6qIIibCsrwtcPEGWBc+8eEW+vdtQonDrVWoUTJzB2LB2pBGFu6ujujrt3k5Lg4AA9PanlkIUiBNCvHxYuRFCQ8G3EwkIsXYpNmxhbSjg5NXbBNTeHiQkFn/DXuTy2bcPHHws/Yu7bF+3bIzJS2qGkRGDtKjX6+rC29jV5RMWDTkx4iRkoWMrQ+yA0NeHmhtu3GUUiFuTGDfj5URGJCqqgCL28cP8+qqsVLYe0JCXRmpbRoKyM8+IFEc8ue9w4HD/e4tX0dDx7JpNftZ8fYmIYtfT0xO3b1CadsbGymr0uXw5XVwQGNrXBqKvD1KmYNk2aUD3Noq4CffpI4BbaEnfuwNPz5UucOoVZs1qsFRyMv/+WdigpoZaHkoenp2vd7bt35RfqikosIwBISqL4QbxamEi5UXfjBvr0oSWS9KiCItTRgZMTw3TpSsOTJ9DRURLnUQBAcjK3e3cxE6t6eEBNrcX536FDmDix6UERFZg/tx0d8eJF0tViCvqrtBRZWVQfqAJwOPjrL7i6onfvNzFIc3IwZgz09LB2rTR9C8kc1Lcvrl+Xpk/U1uLBA7i6hoYiIADGxi1WnDIF586huFiq0aSEWh5KHp6e+hl39PTklxqOmvMqzT3i1+bc0qQ0yc9HYaFSmQ6qgiJEW8jHRCclHkWSkogk69P338f+/ULKCcHevZg+nZpcgvTujcRERkfyampwdy+9eofCpPPmTXh5MbDbFBc1NWzZgrVr8cknsLNDjx5wd0ffvjh0SMrJhZBcsn37Ijpamj6RkgJ7e7Rrt28fZs5sraKREYYOxZEjUo0mDYWFKCuDnR29Hr28kJhIxYNOTOj4FzQ0ICWFriKMiwM8PJCWhooKJl1ER6NPHzFn4fJBiURpDdVXhHRS4lHk7l2JFOHMmQgJEWI7GhUFdXVZbXLo66NrV4YnEZVOXl2KEyhMOq9fl8cezsSJuH8fERHYtQvZ2Vi1SnrV27UrsrIaf2Xu7sjPx/PnzDtNSEDPnunpyMwU7S0zdSpCQ5kPJSW8fxyFAzY+PXsiIYGKB5045Oairo6GIn/0CNbWFO0znZ3x7BmKK7Xg6cnQeuPaNfTvT0seKrCKUE4oRx5mAe7c4UoySXR0RI8eOHCgafnGjVi4kKZcTejXj+EaJrVdz6HGCRSegzduUMupIZKuXeHlJXWI6FdoacHevnGWYjU1+PmJsABunYQEeHsfPIhJk0Rr6lGjkJAgkARDvlDLOsKnQweoqfXt9FQ+K0JqcVdoz8HV1dGzJ27dAvz8GO60s4qQIc7Or7aVVRblUoSEICmJSHh+smwZfvyxUdzRmzeRmNiaxYT0DBqEK1eYNIws8faoi5d2eC4XMTFKdaovEW5uzeJEDxgglSK8dQs+PocOYcoU0XV1dBAQgGPHmI8mDXSyjjShV6+eJF4aY0nxiY2lZKpMJ+p8I17FZmC2015aitRUmZhhS4GKKEI1Ncrh81slLQ3Z2TQ7fP4cpaWQMGyyLElPh5ERacXUQRhDhqBbN/z006u31dWYPx9r11JyWG4B3nObgUfakXsuRuXZ0kbESk6GpaUy2ThJhpDMcYxnFgBqanDv3iO9Hi9eiLtIDgpSmCKUydSzVy+r3ISyMql2l8WEmqmy7BRh//64cUPiP+e1a+jdG5JHDZQpKqIIIWFCIKYkJaFnTwwdil69MHw4nj6l0y3P+kv4Nl1iIv76i/mziRlM/xvbt2PHDmzahJQUTJoEJydZmcnwsbSElZXEpzLFxUhN01Dz8kS8dItC5dvDkQghmeN8fPDgAUpKmHSXkIDu3Q+d1A0KEtfQYeRI3LyJoiImo0lDeTmysmQQyMnbG3FxUuUIE4+GBsTHU1o1yWBG4OuLmBgQC0uYm0vsVn/xIoYMYTx0Sgp27KDvwcIqwjfcuYPhw/HZZ8jKQk4Ohg1D//54/JhCz8Idsuvq8OGHGD8esbFYsAABAQwfTwxgqghtbHD5Mi5cwMSJ8PTE3r3UJROCv/8bzwIxuXIFfn7g+Pky9UMU6GjgQKl6UChCFKGWFnx9GU68YmLg53f0KIKCxG3Rrh38/REezmQ0abh9G+7ukmbsEAMfH8TFySE1XFISbGxa804Rl8ePoa0NKysKMgnQoQP09JCWJnZIfkGkCM+/ahWGD0dmJrPWraFSivDWLV5KAVlQWoqgIGze/MooXEMDy5bhf//DuHGoqpK28xs3hO1yzJmDggLcv4+dO3HnDrp0wZgxUoXvEx8pkjI4OCA8HA8e4IcfZPCgEcawYbhwQbImFy7A318ah/zXqLgitLNDWVmz5Ev+/qLDqAslOrrAoU9uLvr1k6DR+PEIC2MymjRQc0VvgrU1tLUH2Wa2peitAAAgAElEQVTKOuYjNXdzakG7m/JqYSLpLDU/H0+eMLMCWr0ap0/j7l38+CNVY2AAqqQITU1hbS272O8rVsDfH5MnNypcvBjOzvj6a6l65nJx82azn/WOHbh/H4cOvTIR1NDAb7/B3BxffSXVYOJAiMyeEzJh8GDcuCHZdOTCBQwf/tohn/E2SmoqtLWpeqLJGw4HPXo021j290dEBJPurl0Le9F//HjJ/BvHjEFkJIXZpETI8Afu6+vDjZW1ucL165RMlWWW/PaVOfeQIbhyRYI0eefOwd+fgWvQpUv46y+Eh8PMTNKmYqE6ihA03IFb4M4dHDsmPFXq77/jwAEkJjKfgSQlwcqq8ff37BlWrkRISCM7Ew4HO3fiwAGZ2wSlp0NfHxYWsh2FHoaG6NlTgjXM48d4+RI9egA2NmjXrnHATUm4dIlmrmEFIeRAy9sbubkShzN/+BA6OrsudhZ/X5SHiQl69pR33FFqJpfN8fU1S4shRLbxZaKjKSlCmeX8e/UwtrCAvb0EEYFPn0ZAgKRjVVcjOBg7dsgwmatKKcL+/WWkCL/6CitXwshIyCVTU/zwA774QoPxuuLq1Wa7aytX4oMPhEQYMjHBjz9i8WLZRjOU4UNCVoiM+i3ImTOckSNfW3NI4y1w4QKGDmXYVmkQEhJSXR3DhuH8eck6uny5svfgR4+YzA1aj1VLncJCvHhBKQ1hc/z8EBPjK/Xpcyvk5qK8nIb8XC7i42WkCD098fQpCguBkSNx+rRYberqcP48Ro2SdKzNm+HhwUCBSoCqKcKrV6n3GhuLlBR89FGLFWbPRkUF5/hxhp/VpUsYNEjgfWoqwsOxYoXw2tOno7YW//3HbCyxkF0IaZkxbhxOnBB3ehAeznmTCoPBYT6PhgZERbVNRQhg1ChxH158IiOvaw8ZM4bJwfC4cTh5Un5ZmW7cgI+PzAJ4eXsjOXmAT7XsFOHVq+jfn8Yx2P37MDeHqSkFmZqhoQE/P0RHA6NHi0iryefyZTg7S2q5U1yMX3/FunVMhBQflVKEXbuioYGOHacAP/2EL7+EllaLFdTU8P339d9+q87AUofLRVRUY2vhtWvx2WcwNGxxsNWr8d13MlwUyidmGFWcnGBgINaecUkJJyaG8847r98PGsTQMOTWLXTsyMvDrtJ06YLKSjx71rh05EhEREhgmcXlIjJye9owSfdFedjZwcpKhkuoJsg2sYGuLlxchhnekjJ6eStERVGy0IqJkWmqo1cuqX5+ePEC6emiGxw5gokTJR1l0yYEBtJIytgqKqUIAQwcyHCC3wIPH+LGDcyZI6La8OFcMzOEhEjcf3w8rK0FHqdZWThzBp980lqb0aOhqYkTJyQeTBwqKpCaqkxBbsQlKAiHD4uudvKk2uDB5E0Owq5doabWOM6YeJw5g5EjJW6lfHA4ELKPZ2kJZ2cJ/ko3b9abW198YDNiBEMxxo2Tn+0oNUuTlujf3+XltZQU4Xkkpefy5cZ7SIyR8ZR30CBcvoxXuaOPHhVRu64O//2Hd9+VaIiyMmzb1uL2GUVUTREy3ulqgS1bMG8edHVF11y1qn7NGgnMo3icO4c3qxMAmzbhgw9gYCCi2fLlwk13pCc2Fl5esg0GIxt4EZxFbq8dOqQxeXLjxTQD9wswPNVXTvr0ERYScsIECYK+nDqVZDt69Gjm8UDk5kRRW4v4eBlvefTvrxl7zcODadboVsnLQ34+peCgMp4R+Pjg0SMUFbWcm0aQ06fh7AxbW4mG2LkT/v4yXw5C9RTh4MG4fJlWZyUlOHAA8+eLVXnAAGJnJ7EL+cmTGD369ZvSUuzdK1aM6gkTkJ8vkwACV69iwAD63cqe7t3RsaMIC4/sbCQkcAIDGyvCd97B2bOSDZaTg8xMydzllJgBA4SdrQcF4b//xJ3ZnTjxz/NACWfzjejRA3V1svN+ekNCAhwdRU81pWLAAFy/PnhAgyziQV26hIEDaRxwvniBvDyZpgLX0kLfvoiKAgYMQEmJiPRUf/+N2bMl6r++Hlu24IsvpJFRXFRNETo5oboaGRlUOtuzByNGSHAMtHo1vv++UdTp1snNRUaGgN7ZswfDh6NjR9Et1dXx2WfYuFHckcTn8mXV9RCfOxd//tlahb//xqRJDU3X98OH4+pVyRzZjh/H6NEyzEEoX3r3RkoKyssbl3bpAjs7sdwa0tIa8l+EZvVptLchIRwOxo+XrREYj6goSvuKrWBuDhub0Ta36c3J30DNVPnqVfTtK5N82QL4++PCBYDDwQcfYMeOFus9fowbN5q6aYsiLAydOsnI6LUpqqYIORwMHUrFKYkQbN8u4rSuCX37wskJf/8tbv2jRzFmzOvHKSH4/Xd8+qm4jWfPxqVLyMqSQD6RVFUhPl51g2dOnYpr11qcBdXUYMcOzJvXzKLJ2FhiR7YjRzBhAkMplQ8dHXh7C/MimTED+/aJbh8amtQtKHCcmpRxkidOlIcipHbA1jpDhvQqvRQfTz9QQEQEhg+n0ZFcgiINH/46NkNwMA4ebDFB0ObNmDNH0vxiW7Zg8WJpJRQTVVOEAIYNYxgXozFRUdDQkFgprF6NH34Q99d/4ADee+/1m4sXoaMjwXjt22PWLGzbJpl8rRMdDQ8PtG9Ps085oqeHjz/Gjz8Kv7prF3r2hLOzsFPE8eNFH+bzycvD7dttw1KGz5AhwiJhTZmCU6fw8mVrLQnB3r1bCqdNnSqtDH374tkzsawLGVNbi+vX5aQItaMvenpSdmy+dw/q6nByotHX5ctyCAfh6YnSUmRmApaWePddbNggpFJuLvbtw+efS9Tz7dvIysK4cXTkFIkKKsLhw3HxovRBR7dvx7x5Erfy8YGfH7ZuFV3zwQM8fiwwudu+XdzTSD6ffIJdu2iapl24IDqtuHLz2Wc4fhz37jUtLy/H999j1aoWmk2ciJMnxfUWOHgQY8eqoj1RK7zzjrDjVVNTBAZi167WWl65UkO0zpf4SpEw4BXq6pgwAUeOSNtPK8TEwMmJRqxqkQwejOjokf61koYlaJ2zZylNwF68QGamjKKMCsLhYORInDkDAPj6a/z5p5BNrOXL8dFHknoibd2K+fPldzqhgoqwQwfY2AhzEpaAggKcP88whdAPP2D9etEJyf78E7Nmvf4ic3Nx8SKmTZNsJHt79O3LxGmjJc6ehTTnPEqAsTG+/RbBwU0nQitWYPjwlsNLduwIN7fX/1dR7N79KvJ6G8LbG9nZwrJsLl6MrVtbO/feuvVclwXTptE5bHrvPbF8YBgTEQHGDh6SYWKC7t0nWl0/d45mr+HhDOKuCOPiRQwYIB81MmrU6+winTph6VLMmdPIAuvoUVy/jpUrJeqzsBD//ddakBPqqKAiBBAQIHFcjMbs2oWJE1t0am+dbt0wY4YI1xaefeiCBa/f79yJyZOhry/xYIsWYetWOs71OTnIzla54GrNWbAABgb49NM3n8rOnTh9WpRp0axZ2L1bdO+3bqG0tA2EGG2CujpGjhSWDqlXLzg5tfjJPHiAq1e/SJot0tFWTAYMQG4uHj2i01tzzp2jdMAmDiNHds86l5tLLYl3cTHi4ylZylA7aRTNO+8gOvq1KdbSpWjXDtOmoawMAI4exccf49AhvHHsFYudOzFunKziawuHyIXKykpdXV1q3V25Qry8GLduaCBdupCbNyVoUl1dXVNTw39bUkI6dSJRUS3W//57MmPG6zf19aRTJ3LnDiNhCXF3JxERDNsK8scfZNo0wYKqqqra2loKPcudkhIyaBAZPJj89huZNo106UIePHh1qbKysr6+XkibsjJiYkKePhXR9cyZZN06yuJKjclPJp+e+lTKTg4fJiNHCrtw8yaxsSGlpUIuTZqUMuPHfv2kHLkRixaR774jhJCysjIul0ux52fPiLExkd8v+vp14uExfTrZvv1NWanQj1E8/v2XjB1LQS5CCOnU6c1fQmpE3tQ775AjR16/qawkwcHE0JDY2RFnZxIXJ+lw9fWkc2cSHy+5oFKgmivCvn3x9Cnj/PERETA2lsoq18AAf/yBOXNQXCzkal4eNm0SOK8KD4etLTw8GA5Gy4/i+HH5HT3LGAMDXLiAOXOQnIyePZGYiG7dRLVp3x7Tp+P331urk52N8HC57sjIkZEjceOGsGTxPj4YORJfftm0/NQpJCauyFvM4Ci9FaZOFe17zYzTpzF8uJxyZAJA79549mxK3ye04okfPcogAJkwkpKgqSnGX4IaEycKxGbQ1cWffyI9HZGRSElhcE4ZFgZbW7kHv5KPvqW8IiSEzJ5NfvuNWdNx48jOnZI1abIi5LF4MRk1itTVNSrkcsnYseTrrwWKRowg//7LSFJCCCFVVcTKity7x7wHQkhRETEwIGVljTtW1RVhK7S4IiSEZGYSMzPy8mWLjRcsIF9+KSPBpIHKipAQ8u67ZMcOYReKi0nXruSvv96UPHhArKyy/r1qaUmqq6UfuRHdupHYWPorwnHjpPqfMWHWrOpffzM0JEVFrwoYrwjLy4mhISkspCHV2rVk4UIaHb1C5E0VFBBjY1JZSWe4/v0F1pfyQjVXhAAmTJDAIF6ArCxER+P99ymIsH49AEyd+sabghAsW4YXL/DNN68rPXiAu3fBLFYxDx0dfPLJq8EYc+wYhg1TXccJOtjZISgIa9cKv5qaiiNHhCyM2hAzZrQQGsnQEKdO4fvvsXAhoqPx558YPBjr1v14tf+8eczDqrXEzJnYs4dyn+XluHxZIIqTfJgwQfv0f0OGUAgMHBaGfv1gYkJDqhMn8Cb9ijwwN4e3t7ATaMmJjUVODsaPp9CVZMhH39JfEVZVESMjkpcnabsVK8jnn0s8mtAVISGkuprMmEG6dSObNpHdu4m/P+nbl7x4IVBj0aLGy0NGFBaKdb7VCkOHNp9lvXUrQkJIXh6xsBByXtvQQAYMIFu3ylQ2xtBaEdbVkQ4dSEpKC5cLC8kXXxA/P/LuuyQ29tkzYmpKCgqkH7YpT5/yeqa5IjxwgIwaRaszsamsJIaGx3Y85x++Ml4RBgRQWs7m5BBTU7onpeLc1O7ddA44J00imzdT6EdSVFYREkKmT5f0yVVZSSwsyKNHEg/VkiLkERlJ5s8n06aRXbsa75SWlBBTU5KdLfF4zfniC7J4McO2WVnEzKz5DtfbqAgJIfv2ke7dm26QLl1Khg0jDQ0ylY0xtBQhIWTVKrJggVg1Fy9mMmUUk9GjyfbtVRQV4dixZM8eWp1JwuTJNVv/NDYmz54RwlQR5uQQExNSUUFDni1byMyZNDp6gzg3VVZGjI0ZLEwakZpKLCxIeblUnTBDZbdGweTYPSQEvr70Y5n7++OPPxASgtmzG7vu/PMPhg+HjQ2FMZYswb59yM1l0vbvvzFlCv0dLhVl+nQEBsLfH6mpAFBejkWLcOYMQkNllstVifj4Yxw8iPx8EdUeP8a//2L5clmJMX8+du6kZtby4gWuXFFQULwpU7SOHpg4UaxYdS2xaxfefVfSAGQtcPCgQDgr+dG+PYKCRMRmEMlPP+GTTyR1taCEfPStTFaEdXXEyoo8fChmdS6XODuTixeZDNX6ilA4dXXEzo7ExjIZTyhLlog7mRekpqal7bC3dEXIY9s2Ym5OunYlhoZkxow31g5KCcUVISFk8WKyaJGIOhMmkO+/pzWgEBoaiJ1dQ2wsnRXh5s1k+nQqPUlOTQ0xM0sIy+ralXC5TFaE9fXE1pYkJNAQJiODmJtT9yAR86bi4oidHRHnzyeUtDRiZqawP6IqT4E1NDBjBv75R8zqJ0+iXTtIHylKXA4dQufONB3Yly/HkSMS55g9cABubnBxoSZG22DBAuTk4NQpZGZi714YGSlaIPnx1Vc4cEBImDo+R47g/n0sWSJDGdTUMH9+3caNHCq9/fUXPvyQSk+So6WFyZO97u7R15c42ReP//6DrS28vGgIs28fpkyRowdJI3r1go0N86yTq1Zh0SLF/RHlo29lsiIkhKSmEisrIt5azc+PHD7McByJV4RcLvHwIGfOMByvJdavJ6NHS1C/vp50704uXBB68a1eEaoUdFeEhJDt24mPj/D/TXo6sbSULNwEM549KzczI+np0vZz6RJxcSFUHTEkJCGB2Nnt29MwdCiTFaGPDwkLoyFGQwOxs5OFI7r4NxUWRry9mQwRF0esrZu4d8kVVV4RAnBygouLOIm2L1xASQklf1VxOHECmpr0MxgsWoTMTAkSi+/bBwsLSlGbWNoOwcHo2BHBweA2ztWRm4uAAHz7rTySwLVvT1rJJSI+Gzdi4UJw6KwtGeHlBUvLKQan09Nx86ZkIVnPnEFVFSVnh3PnYGYmd0f0Rowdi9paif0oCMHnn2P1aoW6d8lH38pqRUgIOX6c+PqKrNW3L9m/n/kgkq0IGxqIpyc5cYL5eK1w7RqxsSHPn4uuWVJCbGxITExL19kVoapAfUVICCkvJ4MHk3Hj3hg1h4eTTp3IL7/QHadFysrKCgu5ZmZMrLj5JCURa2tqrtzMCQkhw4b99RcZNEiC315DA+nRgxw9SkmGgACyaxelvhoh0TL3xAni7i7ZSeGuXcTHR8Em2yq+IgQwZgyKixEV1UqVkydRXi5pemQpOHgQOjoYM0Ymnffrh+nTMWdO08l8c5YuxejR8PWViRgsKo6eHs6dg5sb3Nzg4YGOHbFiBf7+W7ZHg00wNsbnn4uIX986332H//0Purr0ZGLGu+8iNXVOj8SCAo74Edf+/hvt21Mydk1JQWIipkyh0ZdUBAbCwkJEKENBcnKwbBl27FC0ybZ89K0MV4SEkH/+IUOHtnSxro64uJBTp6QaQYIVYXU16dKFXL4s1XitU1tLBg0iS5e2VufgQeLgQEpKWqnCrghVBVmsCPlUVpLbt0lGhoy6bxFeiLXKSmJnRyIjmfQQE0M6dqTkfic9GzeSSZNOnaqwtW39b/eKnBxiack8FH9Tpk8na9dS6qspkh58pqYSc3OxTn/r68mQIbK1TxYT1V8RApg+HVlZuHRJ6MXff0fHjpSyfInDr7+iRw/ZJsnW1MTRozh9usVEtJGRWLgQR4/CwECGYrC0CXR14ekJe3uFjb5lCxYseBOnUEwaGrBwIX74gZL7nfTMm4fr1wcZJIwaJZB/rQUaGjBjBj79lHko/kakpuL8eXzyCY2+KODkhJUrMXkyqqtF1PziC2hpSbUlQA356FvZrggJIaGhpFev5tvMOTnE3Jzcvy9t9+KuCDMyiJkZycyUdjxxyM8nvXuTiRNJbu6bwvp6snkzsbQkV66I7IBdEaoKMl0RKgrBoNvTppFPJby/9evJkCEKNRZtzh9/1A8ZUllJvLzITz+1VnHBAvLOO8xd7poyYYJMj3YZmMJyuWTaNDJxYtOcBIJ8+y1xc1MWD942sSIE8N570NFp7lM4fz4+/hjdu8tFBkIQHIwvv4SdnTyGs7DA1avo3h2urnj3XaxZg4UL0a0bwsIQHY0BA+QhAwsLDX7//VVsHzG5dQs//4y//1aosWhzPvyQk5+ve/royZP46y+sXSsko3Z9PRYsQFwcDh2CumQWpi0QGYk7d/DppzT6ogaHg3/+QV0dxo7Fy5dNr1ZW4qOP8N9/uHBBWTx424oi5HCwbRtWrsSzZ/yynTuRnY2vvpKXDL/9hvJyfP65vMYDtLTwww9IS8PYsWhogIMDDh/GxYtwcJCfDCwsUmNoiGPHsHhx60Zvr3jyBEFB2LFDYdu5LaKhUb1hAz77zEav+MoVnDiBUaOQmPjqIiG4dAl9+iAnBxcuUDq1qKrCggXYsgU6OjS6o4mWFo4dg6sr3NywYQMyM0EInjzBtm1wcUFtLa5dg6WloqXkI5+Fp8y3Rnl88w0JCOBtl9y+TczNpc3ix0f01mhcHLGwIGlpdMaTC+zWqKrQ5rdGeVy6RCwsyOnTrbVKSyMODmTLFtnKxpjS0lKycCGZOpUQUltLNm4knTsTW1vSqxexsCBubmTfPqrbua/HkimMU2rwSEwks2aRjh0JQDp0IO+/T65doyUaNdqWIqytJf36kbVr8/KIvT0JDaXWsQhFmJtLOnem5xAkJ1hFqCq8JYqQEHL9OunQgXz1lXDXwIMHiZUV+fNPeYjHjNLSUlJZSdzdyR9/8AvT0khcnFRZ1IRz+DCxt5fDIZuUilAl0BC9ZlQhNDVx6BC3t9+vO7vNnh0kJ8fB4mKMHo3gYDnGrWFhaZv06YPERCxaBEdHzJ0Lf3906oSSEsTGYvdu1NTg+HGa4Xtlgq4ujh3DgAFwcMDw4YBsTiri4/HJJzhzRlkO2VSctqUIgeeaHT4zPLH98Uh9n3ZAgMzHKyxEQAAGDZLjUSQLS1vGwgKhoUhOxr59+OYb5OTAwACenvi//8OoUYp2uxYTR0ccPoygIBw+jIED6fd/9y4CA7Fzp2IDqrUlVOJnJS7JyejbFw5BPfQvHsecOQgJke14Dx+iXz8MHYoNG2Q7EAvLW4abG9atw5UrSE9HYiJ278aYMSqiBXn074/QULz7Lo4codzzpUsYMQJbtiAwkHLPbzEq9MtqDS4XmzfD3x/ffYfVqwFfX1y8iG+/xaJFEnvqiklICAYMwJIl+PFHJTPiZmFhUQKGDMG5c/jyS3z+OZ2nUH091q7F1Kk4cACTJlHokOU1FBRhXV1ddnZ2fX299F0xgBCcPg1vb4SF4fp1TJ36+oKLC+Lj8eIFPD1x8iTNIZOS8M47WL8eERGKS4PGwsKi9PTogbg4FBTA3R1HjwrxKxSfixfRuzeiohAXJ8esqm8L0irC06dP29jYDB482NbW9lILQc5kRFYWNmyAmxu++gorV+LSJTg6Nq5haIj9+7FlC776Cn5+CA1FTQ3z8bhcREZi4kSMGIExYxAXRyk+EgsLS9vFxAT//ovt27FuHdzd8fvvePFCguZlZdi7F3364OOPsWIFzp2DjY3MZH17kcpYpqamZs6cOf/888+YMWNCQ0PnzJmTnp6uTiNeQkwMNm6Euzvs7GBpifbtoaaGsjIUFODJE6SkIDYWpaUIDMQff4g6jR45EiNG4PhxbNuGTz9FQABGjEC/fujSRSxRcnMRE6Nx7pxaeDgsLfHRRwgJUZr4hiwsLKrAsGEYNgyXLuGvv/D113Bzw8CB6NkTTk6wtYWh4Zua5eV4+hSPHuH2bVy9ips3MXgwli9HYKBKnZGqGBwixWo9PDx80aJFGRkZALhcbocOHUJDQwcPHty8ZlVVlampaWVlpZg9l5XhxAmkpODJExQUoKwMHA709GBmhk6d4OwMHx+4ukp+Npebi5MnERmJGzdQXg4nJ9jbw9ISxsZvQjPU1eHlSzx/jseP8eABCIGvb/2AAWT0aE03NwnHU2qqq6vV1dU1NTUVLQhNqqqqtLS0qMzGlAfTdaZT3aduHbVV0YLQpLy8XE9Pj9O2ztfLysr09fVFVKquRnQ0rl3DnTt48ADZ2Sgvf6ULy8qgrQ0bG3TtCg8P9OmDIUMUmq+WJ5QYN6XiSLUizMzMdHJy4r1WU1NzcHDIzMwUqggBEEJ4KpOHqampoeA8qDH6+pg2TRrRWqBDB8ybh3nzAKCwEA8eICsLeXkoLkZR0as6GhqwsYGXF2xt4eQEKysADTU1bewfy8LCohh0dDB0KIYOfVPS0IDSUgBo3x5ta2KqKkilCEtLS3UFcmLq6emV8r7OZhBCamtrhwp899OmTVu2bJk0o0uLlhbc3eHuLqJaWRmAmpoaDoejpaUlD8HkBbsiVBUIIXV1dWVlZYoWhCYVFRVcLreNzS/Ly8sZttTQAIDqatG5i+QO85tSDnR0dEQ+5aRShBYWFsXFxfy3RUVFFhYWQmtyOBxtbe3MzExphlMgWlpabU8Rampqtj1FqKGh0fYUIYfD0dTUbGPbUxwOp+1tjQJoY18TjzZ5U4JIdfrq4eGRmJhYV1cHoLKyMiUlxYM1pGRhYWFhUSmkUoS+vr4ODg5ffPHFvXv3Fi9e7Ovr6+rqSksypeLhw4dpaWmKloIyycnJT548UbQUlLl9+3Zubq6ipaBMfX19tfLtmEnJ9evXS0pKFC0FZaKioioqKhQtBWUuXrxYI43jmSogrT1uWFhYUVHRzJkzuVzuwYMHqcikhISGhh4+fFjRUlBmz549J06cULQUlNm+fXtERISipaBMdXV129PuP//8c2xsrKKloMzq1auTkpIULQVlli9fnp6ermgpZIu0Qbc7duy4b98+KqIoOdL4mSgt7E2pCm3yplhYlATWQ5OFhYWF5a2GVYQsLCwsLG81UkWWEZ+qqioDA4OWfO2Vn/T0dDU1NXt7e0ULQpMHDx7o6ura2toqWhCapKSkGBsbd+jQQdGC0OSK/RXTIlPX4jZliZaQkGBnZ2diYqJoQWhy69YtJycnAwMDRQtCkxs3bnh4eOjp6SlaEIZMmDDh448/br2OnBQhgL1796ru46moqIjD4Ri1rWTQL1680NbWbmMeQvn5+fr6+u3aVjDY3NxcExMTHX4UwDZBdna2paVlG3NjffLkSYcOHTQ02lTC88ePH3fu3Fl1PT7t7e0dHBxaryM/RcjCwsLCwqKEsGeELCwsLCxvNawiZGFhYWF5q2EVIQsLCwvLWw2rCFlYWFhY3mrUv/32W0XLoNTU1NScOnUqLi7O0tKyfbMMmVwuNzk5+dKlS4mJiZqamubm5goRUlLq6urOnj0bExNjamoq1NQ7Nzc3IiLi9u3b7dq1UxUD96qqqvDw8ISEBGtra6Gm3oSQ9PT0hIQEc3NzbW1t+UsoDrW1tadPn75586aFhUVLBr03b948f/48IcTa2lrO4jGjoaEhIiIiOjrayMioJdPr7OzsuLg4XV3d5v8y5UTkTeXn51+4cCE+Pl5HR8fU1FT+EjKgsrLy5MmTiYmJ1tbWzU2vGxoa7t69e/HixfrpxnsAABTISURBVLt376rQTYkFYWmZysrKXr16DR48eObMmaampnfu3GlSITU11cXFZfr06TNmzDA1NV25cqVC5JSIurq6gQMH9unT54MPPjAxMYmOjm5S4fTp08bGxuPHj586daqBgcG2bdsUIqdElJWVubu7Dxs2bPr06ebm5vfv329Soby83MjIyNTUlMPhJCYmKkRIkVRXV/v5+Q0YMGD27NkmJiZxcXHN66xatapz587z5s3r2LHjL7/8In8hJYXL5Y4ePbpXr14ffvihqanpuXPnmtfx9PTU19fX0dHZv3+//CVkAJfLDQgI6NWr19y5c01NTSMiIppUuHr1qpGR0dixY2fMmGFkZPTzzz8rRE6JKCkpcXFxGTFixNSpUy0sLB4+fNikwq1bt9zd3WfMmDFt2jRjY+N169YpRE5ZwCrC1vj77799fHzq6+sJIV9//fW7777bSuXo6GgtLa2amhp5SceQo0ePdu/enSfn+vXrhw0b1qRCXl5eWVkZ7/V///1naGjI5XLlLaWEbN26tX///g0NDYSQ//3vfzNnzmxSob6+PjMzkxCira2ttIowJCTE09Ozrq6OELJ69eqxY8c2qfD8+XNdXV3eE+rOnTv6+vqlpaUKEFQSIiMjO3bsWFFRQQjZuXOnj49P8zoZGRkNDQ1eXl6qoggjIiI6derEu6k///zT19e3SYXnz58XFxfzXkdGRmprayv/k2Hjxo1Dhgzh/dkXLlw4d+7cViqfPXtWJZ4MYsKeEbZGeHj4hAkTeFleJ02aFB4eTlp2u6ysrDQyMlJ+X9rw8PDAwEBekuFJkyZFRkZWVVUJVhDcBLa2tq6vr+dyuQoQVBLCw8ODgoLU1NTw+ptqUkFdXd3Ozk4BkklCeHj4+PHjeT+hSZMmnT59uqGhQbDChQsXnJycunbtCsDDw8PKyury5csKEVV8wsPDAwICePtsQUFBt27dysvLa1LH3t6e992pCuHh4aNGjeLd1KRJk2JjYwsKCgQrmJmZGRoa8l5bW1s3NDQ0+SqVEN6fiOc4L/RPJEhFRQVvf0Ve0skWVfrxyZ+cnBwbGxveaxsbm6qqqpcvXzavNmnSpEGDBn300UdhYWHK/38WvClerJ+WUvwQQtasWTN79mzlT/je5Jt6+fJlE+2uEjS5i/r6+vz8fMEK2dnZHTt25L+1sbHJycmRq4iSI3hTRkZGenp6yi+zSARvysTERFdXt5WbWr169fvvv6+rqysv6RjS5OdXUFBQW1vbvNqoUaMGDBiwbNmyo0ePyldAGaLsyxdZc/Xq1YULFzYvP3PmDG8ex1dsPGVQX1/fvPKiRYuKiop27ty5atWqs2fPKlwX3r59e/bs2c3LDx486OTkJHhTampqHA5H6E0B+PLLLwsKCkJDQ2UnqvhEREQsXbq0efmlS5eMjY3F/KaUHJF30dDQIDgH19DQUP7bFLwpqIjMImlyU+rq6i3d1Jo1a5KTk69cuSIv0ZjT5OdHCBG6FbRkyZKSkpJt27atWbOmzejCt10R9ujRIyQkpHm5mZkZAGtra/6OR35+fkt2oQMHDgQQEBDAM3Do3bu3LEUWTbdu3YTeVOfOndH4pp4/f87lcoXGgP3mm28iIiIiIyOVxIrP19dX6E3xrF6bfFP6+vqqGEO1yV2oqalZWVm1VIFXR/nj9wrKXFVVVVpaqvwyi0TwpioqKsrLy4Xe1Pr160NCQqKiooyNjeUrIBOa/PxaCm/r7+8PYPjw4cbGxqmpqd27d5erlDJCwWeUys369etHjBjBe71p0ya+XUlxcXF1dXWTys+fP9fU1Gxur6hs7Ny509fXl3fKvWfPnl69evHKS0pKqqqqeK9//vlnJyenZ8+eKUxKCVm9evW4ceN4r3/88cfAwEDe66KioibflDIby2zdunXQoEG813/88ceAAQN4r/lfTWZmpra2dkFBASHk6dOnOjo6eXl5ChJWXI4cOdK9e3eeHVNYWFjXrl15v73S0lKesQkfFTKWOXTokIuLC++mjh071q1bN/5NVVZW8ups3rzZzs7u8ePHihRUEr755ptJkybxXq9evXrixIm810VFRc0tfbKysjgcTk5OjlxFlBmsImyNwsJCGxub4ODgtWvXGhkZRUZG8so9PDz++OMPQsju3bunT5/+008/ffPNN926dXvvvfeU34yqoqLC0dFxxowZP//8s5mZ2bFjx3jlgwYN+vHHHwkhJ06cADBu3Ljg1xQVFSlUZNHk5eVZWlp+8skn33//vaGh4bVr13jlXbt23b17N+/18uXLg4OD1dXVg4KCgoODS0pKFCevcIqLizt37vzBBx/89NNPJiYmZ86c4ZX7+Phs2rSJ93rmzJm+vr6bNm3q2bPnJ598ojhhxaW2ttbd3T0oKOjXX3+1trbetWsXr3zcuHHLli3jvd68eXNwcLCZmdnQoUODg4NTU1MVJq541NbWurm5TZo0af369dbW1nv27OGVBwYGrlixghBy+fJlDocTEBDA/xMp/7QyJyfH3Nx80aJFq1evNjQ0jImJ4ZXb2dn9+++/hJDffvtt1qxZ69atW7lypb29/YcffqhQeWnCZp8QQV5e3t69e8vLy8eNG9erVy9e4bFjx5ydnZ2dnQsLC0+ePJmWlqatre3j4/POO++ohBlVYWHh7t27i4qKRo0a1bdvX17hqVOnOnbs6Onp+eDBg6ioKMH606ZNU/5sZDk5Ofv27auqqpo4caKnpyev8NChQ15eXjwzy/3795eXl/PrT58+XQmzNRUUFOzZs6e0tDQwMJC/x378+HFHR0dXV1cADQ0N//7777179zw9PSdPnqzwA2lxKCkp2b17d0FBwbBhw4YMGcIrjIiIMDIy8vHxAXDmzJmnT5/y648ZM0b5t09LSkp27dr1/Pnz4cOH8zOtnj9/3sTExNvbOzMzMyIiQrD+e++9p/x53J4+fRoSElJTUxMUFOTu7s4rPHjwoLe3t4ODQ35+fnh4eEZGRrt27fz8/IYOHapYaSnCKkIWFhYWlrcaFZhOsrCwsLCwyA5WEbKwsLCwvNWwipCFhYWF5a2GVYQsLCwsLG81rCJkYWFhYXmrYRUhCwsLC8tbDasIWVSbkpKSPXv2ZGdnK1qQ1nj69CnPO1DRgsiburq6w4cPv3jxglnzmpqaw4cPC410z8JCEdaPkEW1uXfvnqur64kTJwIDAxUtS4scP358/Pjx9+/fV7nAjEOHDs3MzBQs0dbWvn//vpjNt2zZsmHDhocPH/LSfk2ePPnWrVtN6vTp0+fff/9tRQBnZ+fffvtNQsFZWCTgbQ+6zaLqWFhYrFmzRuUUjKrw5MkTQsjcuXP5JeJn3CwvL1+9evXatWt5WhBAbm5uWVnZ4sWLBau1nidy5cqVI0aM+Pzzzx0cHCQTnYVFbFhFyKIyVFdXFxcXGxsba2tr8wvNzMxWrlzZvGZJSYmZmVlLmRSrqqpKSkpMTEz4z+gmlJaWqqurMwgs9/z5cwMDA0EJGVNeXl5eXm5ubt78Lurq6l6+fGloaCg0P0DrVFZWFhcXt5RboDm2trbNP2Fx2L9/f0VFxXvvvSdYaGpqKlFvgwcPtrW13b59+y+//MJABhYWcWDPCFlUgLy8vClTphgZGVlbWxsaGs6aNYsfNfTRo0fW1tb8uI4NDQ1LliwxMTGxsrLihXgeOXLktGnT+F2lp6ePGTPGwMDA2traxMRk8eLF/OyjJ0+eNDExuXz58rBhwwwNDQ0MDPr06ZOVlcW72r9//0mTJglKVVpaam1t/f333wPIyckJDAzU09OzsLDQ09Nzc3M7ffp0S7fj4OCwevVqwZIuXboIlsTGxvbt25cnpIWFxS+//MI/wsjIyBgxYoSOjo6VlZWurq6zs3NKSoqYH2N0dHSPHj309PRsbGzat2/v5uYmZkNm7N27d9iwYVIG2ORwOOPHj9+7dy97iMMiO1hFyKLslJeX+/v7x8XFhYSEpKSk7Nq16+zZs1OnTuVdraury8vLq66u5r1ds2bNxo0bly5dmpSUtGfPnnXr1t28ebOiooJ3NT8/f+DAgdnZ2WFhYSkpKRs2bNi1a9eiRYt4V2tra4uKimbPnu3v7x8bG7tv377U1NTPPvuMdzUwMDAsLOzZs2d8wQ4dOpSXlxcUFASgpKTE1tb2v//+u3//fmRkpK2t7cSJEx88eCD0joqLi6uqqloquXPnzpAhQ9q3bx8ZGZmUlPS///1vxYoVW7Zs4V2dNWtWXl5eREREenr6jRs3Zs+eLTR7anO4XO6ECRMAnD17Nikp6fz58zNnzhSnIYB6AcRUSJWVlbdu3erTp4+YQ7RCnz59CgoKkpOTpe+KhUU4Csx8wcIiDhs2bFBTUxNM9Hjw4EEAvBLeeujEiROEkJqaGgMDg6lTp/JrJiQkAOCnKlyyZIm+vr5gQpyNGzdqaGgUFhYSQo4cOQJg1apV/KsrV67U0NCoq6sjhOTk5Kirq/MWZzz69+/v5+cnVOaqqipjY+MffviB9zYsLIwvMCHExMRk+fLlgvWNjY35JaNHj+7atSs/NyQhJDg42MbGhvdaR0fn559/Fv2pNaO4uJjD4fz1118StXJ0dGzyxFi3bp04DW/evAng8OHDgoX9+/dv/gjifeAZGRlDhgwR2tXdu3cB7Nu3TyLJWVjEhz0jZFF2zp07Z2Njk52d3cRHIjk5uYmNTFZWFi+BEb/Ey8urU6dOgl05OjomJyfzlxfa2tr19fX379/v168fryQgIIBf38XFpb6+Pjc319bWtkOHDiNGjNi9e/eSJUsAZGZmRkdHb9u2jV+5uLg4NDQ0PT2d5yahrq6elpYm6c3W19dHRkaOGDHi2rVr/EITE5OcnJyXL1+amJh4eXmtX7++oqIiKCjIzc2tpbRfZWVlfG8NLS0tc3NzQ0PDL7744v/+7/8KCwvt7OycnZ09PDzEEcnZ2fnbb7/lv+WnuGodnsuEiYlJk3Jra+v169cLlvDS/XC53LKyMgDl5eXt27cXrGBqagqAnzydhYU6rCJkUXby8/Pz8vKa2FwYGxsXFhY2qZmXlwfAzMxMsNDc3Jz/uqCgoLi4uPWujI2N+a95pjT8Q8RZs2ZNmTIlPj6+V69eu3fv1tbWnjx5Mu9STEzMyJEjjY2NhwwZYmJioqGhoampycBxsLS0tLq6OiIi4urVq02ELCgoMDExCQ0NXbp06S+//PLdd9/xskavWLFCU1OzST8bNmzgay9vb2+e08KoUaPOnTu3evVqS0vLjz76SExFaG5u3uQTEweecWl9fX2Tct6SXWiTkpKSjz76qKKi4u7duydPnrS3t+eV19XVAWh+jywstGAVIYuyY2ho6OzsfOfOHZE1bWxs8Fod8nn27Bl/UWhgYNCjR4+zZ88yk2TcuHHGxsZ79uzp2bNnSEjI+PHj+Vrzl19+sbGxiY+P55tiHjhwoKV+eNut/LcNDQ38U0w9PT11dfV58+Zt3LhRaFtbW9uDBw9WV1fHxMTs379/1apV2tray5Yta1ItODh4zJgx/D4BZGRkBAQELF68eM2aNS3ZylLE0tISQPPJSisUFhZu3Lixffv2v/zyy759+7755ht+OQArKytZyMnCAtZYhkX5GTRoUEpKiji2kba2tubm5rwTRB5XrlwRNG8ZNGhQdHR0bm4uM0l0dHSmTJmyf//+8+fPZ2RkzJo1i38pMzPTzc2NrwXv3r0rmHK9CTY2Nunp6fy3N27c4C86tbW1/fz8Tp482cSaprkkgwcP3rFjh6ura0xMTPMK1tbWvV7D20BOSUmpqamZO3euHLQgABcXF319/aSkJPGb2Nvb8zZFO3ToIBhNhndG2Lt3b+pCsrDwYBUhi7KzcOFCKyur8ePHnzlzpqSk5MWLF9euXZs/f37zyFsaGhorV64MDw+fO3fuuXPndu7cOW3aNMGVxIoVK9TV1ceMGRMVFVVeXp6XlxcZGTl79mzxhZk1a1ZhYeH8+fM7dOgwfPhwfjlvoRkVFVVTUxMTE/P++++3a9eupU5GjBhx6tSpQ4cOPX/+PDIycsGCBYL7fj/88ENWVtbEiRPj4+Orqqp4Nq68g8ni4uL58+dHR0cXFxfX1NScPHkyPT29V69e4kjeo0cPDQ2N5cuXP3r0iMvlFhcXnz179uHDh+Lfu0RoaGgMHDgwOjq6SXlZWdnBxpw8eZJ3qaXzzuvXrzs6Onbu3FlGorKwsFajLCpAWlra0KFD+T9aDQ0Nf3//0tJS0thqlMfGjRs7d+6soaHh7u5+6tQpDw+PDz74gH81MTFRcG2hra09evRo3iWe1eiDBw/4lXkljx49EhTG2dkZwLJlywQLc3Nzvb29eX3q6upu3Lixd+/eQUFBvKtNrEZfvnzp7+/Pq2xsbBwWFiZoNUoIOXPmTNeuXflC6uvrL1y4kBBSWlrq6urKVxiamppz586tqakR82MMDQ3t0qULv1tdXd2oqKjWmzg6Og4cOFDM/ptw7NgxNTW1p0+f8kuEWo3yDGLT0tK8vb151UJCQhYvXsx7XVNTY2Zm9tNPPzGTgYVFHNhYoywqQ0FBwePHj/X09GxtbfX19fnlDQ0NLUWQKSsrs7KyWrFiRZNoJjk5OTk5Ofr6+nZ2drq6ulTE43K5GRkZJSUlTk5OTewehQqZlZVVWFjo7OzckgBpaWkvX740Nja2s7MTXDIWFhZmZ2cTQuzt7Q0NDSUV8unTp/n5+e3bt3d0dBS5TcpzUlRTY7J1VF9f7+LiMn36dP5pH5fLbf7A4XA4rfR/8ODB4ODg9PT0JjZQLCwUYRUhS5siPz//yZMnPj4+AMrLy+fPnx8aGpqYmMiz0WeRM2FhYXPmzMnIyBC0xRUfLpfbo0ePadOmNbcGYmGhCKsIWdoUt2/f9vLyMjMzMzU1zczMVFNT+/XXXz/++GNFy6W8xMbGCs1zZGFhIebpY+tkZGRYWVm1cmLaCvX19U+ePOnUqRPrO8EiU1hFyNLWePr0aUJCwosXL0xNTfv16yfoR8jSnKlTpzZPjQTA39//zz//lL88LCzyh1WELCwsLCxvNaz7BAsLCwvLWw2rCFlYWFhY3mpYRcjCwsLC8lbz/zRlLjAsiStkAAAAAElFTkSuQmCC", + "image/png": 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Hamiltonian kblocks: 31%|████▉ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 37%|█████▉ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 44%|███████ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 48%|███████▊ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 53%|████████▌ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 60%|█████████▌ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 65%|██████████▍ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 69%|███████████▏ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 74%|███████████▉ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 81%|████████████▉ | ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 87%|█████████████▉ | ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 94%|███████████████ | ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 98%|███████████████▊| ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 100%|████████████████| Time: 0:00:02\u001b[K\n" + "\rDiagonalising Hamiltonian kblocks: 3%|▌ | ETA: 0:00:04\u001b[K\rDiagonalising Hamiltonian kblocks: 8%|█▎ | ETA: 0:00:03\u001b[K\rDiagonalising Hamiltonian kblocks: 13%|██▏ | ETA: 0:00:03\u001b[K\rDiagonalising Hamiltonian kblocks: 16%|██▋ | ETA: 0:00:03\u001b[K\rDiagonalising Hamiltonian kblocks: 19%|███▏ | ETA: 0:00:03\u001b[K\rDiagonalising Hamiltonian kblocks: 24%|███▉ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 29%|████▋ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 34%|█████▍ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 39%|██████▎ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 44%|███████ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 48%|███████▊ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 53%|████████▌ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 58%|█████████▎ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 63%|██████████▏ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 68%|██████████▉ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 73%|███████████▋ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 77%|████████████▍ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 82%|█████████████▏ | ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 87%|█████████████▉ | ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 92%|██████████████▊ | ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 95%|███████████████▎| ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 100%|████████████████| Time: 0:00:02\u001b[K\n" ] }, { "output_type": "execute_result", "data": { "text/plain": "Plot{Plots.GRBackend() n=98}", - "image/png": 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", + "image/png": 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"\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" ] }, "metadata": {}, diff --git a/dev/examples/collinear_magnetism/cd8e0e04.svg b/dev/examples/collinear_magnetism/516784c5.svg similarity index 54% rename from dev/examples/collinear_magnetism/cd8e0e04.svg rename to dev/examples/collinear_magnetism/516784c5.svg index 9796766050..abca3bd8da 100644 --- a/dev/examples/collinear_magnetism/cd8e0e04.svg +++ b/dev/examples/collinear_magnetism/516784c5.svg @@ -1,54 +1,54 @@ - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/examples/collinear_magnetism/864a3d8f.svg b/dev/examples/collinear_magnetism/900b91a8.svg similarity index 72% rename from dev/examples/collinear_magnetism/864a3d8f.svg rename to dev/examples/collinear_magnetism/900b91a8.svg index 3868c8eece..0628ea7c23 100644 --- a/dev/examples/collinear_magnetism/864a3d8f.svg +++ b/dev/examples/collinear_magnetism/900b91a8.svg @@ -1,593 +1,593 @@ - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/examples/collinear_magnetism/index.html b/dev/examples/collinear_magnetism/index.html index 3855a858b1..70df5cdc25 100644 --- a/dev/examples/collinear_magnetism/index.html +++ b/dev/examples/collinear_magnetism/index.html @@ -13,40 +13,40 @@ scfres_nospin = self_consistent_field(basis_nospin; tol=1e-4, mixing=KerkerDosMixing());
    n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime
     ---   ---------------   ---------   ---------   ----   ------
    -  1   -16.65000202077                   -0.48    6.5
    -  2   -16.65069816737       -3.16       -1.01    1.0   17.3ms
    -  3   -16.65080993145       -3.95       -2.31    1.8   19.7ms
    -  4   -16.65082417445       -4.85       -2.80    2.8   23.9ms
    -  5   -16.65082464643       -6.33       -3.29    1.5   19.4ms
    -  6   -16.65082469308       -7.33       -3.82    1.8   20.8ms
    -  7   -16.65082469755       -8.35       -4.29    2.0   22.3ms
    scfres_nospin.energies
    Energy breakdown (in Ha):
    -    Kinetic             15.9209047
    -    AtomicLocal         -5.0693583
    -    AtomicNonlocal      -5.2202632
    +  1   -16.65004879647                   -0.48    5.8
    +  2   -16.65071352825       -3.18       -1.01    1.0   33.2ms
    +  3   -16.65081056636       -4.01       -2.32    1.2   34.5ms
    +  4   -16.65082414092       -4.87       -2.83    2.8   46.8ms
    +  5   -16.65082460581       -6.33       -3.27    1.2   35.6ms
    +  6   -16.65082468888       -7.08       -3.77    2.0   43.1ms
    +  7   -16.65082469751       -8.06       -4.27    2.0   45.0ms
    scfres_nospin.energies
    Energy breakdown (in Ha):
    +    Kinetic             15.9207992
    +    AtomicLocal         -5.0692997
    +    AtomicNonlocal      -5.2202047
         Ewald               -21.4723040
         PspCorrection       1.8758831 
    -    Hartree             0.7793543 
    -    Xc                  -3.4467534
    +    Hartree             0.7793371 
    +    Xc                  -3.4467479
         Entropy             -0.0182879
     
    -    total               -16.650824697549

    Since we did not specify any initial magnetic moment on the iron atom, DFTK will automatically assume that a calculation with only spin-paired electrons should be performed. As a result the obtained ground state features no spin-polarization.

    Now we repeat the calculation, but give the iron atom an initial magnetic moment. For specifying the magnetic moment pass the desired excess of spin-up over spin-down electrons at each centre to the Model and the guess density functions. In this case we seek the state with as many spin-parallel $d$-electrons as possible. In our pseudopotential model the 8 valence electrons are 2 pair of $s$-electrons, 1 pair of $d$-electrons and 4 unpaired $d$-electrons giving a desired magnetic moment of 4 at the iron centre. The structure (i.e. pair mapping and order) of the magnetic_moments array needs to agree with the atoms array and 0 magnetic moments need to be specified as well.

    magnetic_moments = [4];
    Units of the magnetisation and magnetic moments in DFTK

    Unlike all other quantities magnetisation and magnetic moments in DFTK are given in units of the Bohr magneton $μ_B$, which in atomic units has the value $\frac{1}{2}$. Since $μ_B$ is (roughly) the magnetic moment of a single electron the advantage is that one can directly think of these quantities as the excess of spin-up electrons or spin-up electron density.

    We repeat the calculation using the same model as before. DFTK now detects the non-zero moment and switches to a collinear calculation.

    model = model_LDA(lattice, atoms, positions; magnetic_moments, temperature=0.01)
    +    total               -16.650824697513

    Since we did not specify any initial magnetic moment on the iron atom, DFTK will automatically assume that a calculation with only spin-paired electrons should be performed. As a result the obtained ground state features no spin-polarization.

    Now we repeat the calculation, but give the iron atom an initial magnetic moment. For specifying the magnetic moment pass the desired excess of spin-up over spin-down electrons at each centre to the Model and the guess density functions. In this case we seek the state with as many spin-parallel $d$-electrons as possible. In our pseudopotential model the 8 valence electrons are 2 pair of $s$-electrons, 1 pair of $d$-electrons and 4 unpaired $d$-electrons giving a desired magnetic moment of 4 at the iron centre. The structure (i.e. pair mapping and order) of the magnetic_moments array needs to agree with the atoms array and 0 magnetic moments need to be specified as well.

    magnetic_moments = [4];
    Units of the magnetisation and magnetic moments in DFTK

    Unlike all other quantities magnetisation and magnetic moments in DFTK are given in units of the Bohr magneton $μ_B$, which in atomic units has the value $\frac{1}{2}$. Since $μ_B$ is (roughly) the magnetic moment of a single electron the advantage is that one can directly think of these quantities as the excess of spin-up electrons or spin-up electron density.

    We repeat the calculation using the same model as before. DFTK now detects the non-zero moment and switches to a collinear calculation.

    model = model_LDA(lattice, atoms, positions; magnetic_moments, temperature=0.01)
     basis = PlaneWaveBasis(model; Ecut, kgrid)
     ρ0 = guess_density(basis, magnetic_moments)
     scfres = self_consistent_field(basis, tol=1e-6; ρ=ρ0, mixing=KerkerDosMixing());
    n     Energy            log10(ΔE)   log10(Δρ)   Magnet   Diag   Δtime
     ---   ---------------   ---------   ---------   ------   ----   ------
    -  1   -16.66158880210                   -0.51    2.618    4.8
    -  2   -16.66809122385       -2.19       -1.09    2.445    1.4   36.9ms
    -  3   -16.66904441858       -3.02       -2.05    2.337    2.1   43.2ms
    -  4   -16.66910037426       -4.25       -2.74    2.302    2.0   42.9ms
    -  5   -16.66910315010       -5.56       -2.98    2.295    1.6   70.2ms
    -  6   -16.66910415078       -6.00       -3.52    2.287    1.9   41.1ms
    -  7   -16.66910417187       -7.68       -3.81    2.286    1.9   41.9ms
    -  8   -16.66910417420       -8.63       -4.31    2.285    1.6   41.3ms
    -  9   -16.66910417509       -9.05       -4.85    2.286    1.6   40.9ms
    - 10   -16.66910417507   +  -10.75       -5.30    2.286    1.9   43.3ms
    - 11   -16.66910417509      -10.69       -5.83    2.286    1.5   41.0ms
    - 12   -16.66910417508   +  -11.07       -6.23    2.286    1.5   41.0ms
    scfres.energies
    Energy breakdown (in Ha):
    -    Kinetic             16.2947192
    +  1   -16.66157810052                   -0.51    2.618    5.2
    +  2   -16.66810751573       -2.19       -1.09    2.446    1.4   71.2ms
    +  3   -16.66904364730       -3.03       -2.05    2.338    2.1    147ms
    +  4   -16.66910003735       -4.25       -2.73    2.302    1.9   77.4ms
    +  5   -16.66910311625       -5.51       -2.97    2.295    1.9   79.9ms
    +  6   -16.66910415476       -5.98       -3.53    2.286    1.8   78.0ms
    +  7   -16.66910417183       -7.77       -3.82    2.286    1.8   78.1ms
    +  8   -16.66910417443       -8.59       -4.33    2.286    1.9   83.5ms
    +  9   -16.66910417505       -9.20       -4.94    2.286    1.8   81.4ms
    + 10   -16.66910417507      -10.82       -5.30    2.286    2.0   84.3ms
    + 11   -16.66910417509      -10.68       -5.90    2.286    1.6   79.7ms
    + 12   -16.66910417508   +  -11.29       -6.21    2.286    1.8   83.1ms
    scfres.energies
    Energy breakdown (in Ha):
    +    Kinetic             16.2947193
         AtomicLocal         -5.2227261
         AtomicNonlocal      -5.4100280
         Ewald               -21.4723040
    @@ -55,17 +55,17 @@
         Xc                  -3.5406834
         Entropy             -0.0131612
     
    -    total               -16.669104175083
    Model and magnetic moments

    DFTK does not store the magnetic_moments inside the Model, but only uses them to determine the lattice symmetries. This step was taken to keep Model (which contains the physical model) independent of the details of the numerical details such as the initial guess for the spin density.

    In direct comparison we notice the first, spin-paired calculation to be a little higher in energy

    println("No magnetization: ", scfres_nospin.energies.total)
    +    total               -16.669104175084
    Model and magnetic moments

    DFTK does not store the magnetic_moments inside the Model, but only uses them to determine the lattice symmetries. This step was taken to keep Model (which contains the physical model) independent of the details of the numerical details such as the initial guess for the spin density.

    In direct comparison we notice the first, spin-paired calculation to be a little higher in energy

    println("No magnetization: ", scfres_nospin.energies.total)
     println("Magnetic case:    ", scfres.energies.total)
    -println("Difference:       ", scfres.energies.total - scfres_nospin.energies.total);
    No magnetization: -16.650824697549325
    -Magnetic case:    -16.669104175082683
    -Difference:       -0.018279477533358346

    Notice that with the small cutoffs we use to generate the online documentation the calculation is far from converged. With more realistic parameters a larger energy difference of about 0.1 Hartree is obtained.

    The spin polarization in the magnetic case is visible if we consider the occupation of the spin-up and spin-down Kohn-Sham orbitals. Especially for the $d$-orbitals these differ rather drastically. For example for the first $k$-point:

    iup   = 1
    +println("Difference:       ", scfres.energies.total - scfres_nospin.energies.total);
    No magnetization: -16.650824697512583
    +Magnetic case:    -16.66910417508362
    +Difference:       -0.018279477571038427

    Notice that with the small cutoffs we use to generate the online documentation the calculation is far from converged. With more realistic parameters a larger energy difference of about 0.1 Hartree is obtained.

    The spin polarization in the magnetic case is visible if we consider the occupation of the spin-up and spin-down Kohn-Sham orbitals. Especially for the $d$-orbitals these differ rather drastically. For example for the first $k$-point:

    iup   = 1
     idown = iup + length(scfres.basis.kpoints) ÷ 2
     @show scfres.occupation[iup][1:7]
    -@show scfres.occupation[idown][1:7];
    (scfres.occupation[iup])[1:7] = [1.0, 0.9999987814396355, 0.9999987814396355, 0.9999987814396355, 0.9582253097538278, 0.9582253097538272, 1.1266067182144539e-29]
    -(scfres.occupation[idown])[1:7] = [1.0, 0.843894473047007, 0.8438944730470093, 0.8438944730470107, 8.140942910724036e-6, 8.140942910723864e-6, 1.3486249086658727e-32]

    Similarly the eigenvalues differ

    @show scfres.eigenvalues[iup][1:7]
    -@show scfres.eigenvalues[idown][1:7];
    (scfres.eigenvalues[iup])[1:7] = [-0.06935854985929732, 0.35688561539051167, 0.35688561539051156, 0.35688561539051206, 0.46173608467246413, 0.4617360846724643, 1.1596215823166038]
    -(scfres.eigenvalues[idown])[1:7] = [-0.03125746678960406, 0.4761890552409885, 0.4761890552409883, 0.4761890552409882, 0.610249971525429, 0.6102499715254293, 1.2269003823420284]
    ``k``-points in collinear calculations

    For collinear calculations the kpoints field of the PlaneWaveBasis object contains each $k$-point coordinate twice, once associated with spin-up and once with down-down. The list first contains all spin-up $k$-points and then all spin-down $k$-points, such that iup and idown index the same $k$-point, but differing spins.

    We can observe the spin-polarization by looking at the density of states (DOS) around the Fermi level, where the spin-up and spin-down DOS differ.

    using Plots
    -plot_dos(scfres)
    Example block output

    Similarly the band structure shows clear differences between both spin components.

    using Unitful
    +@show scfres.occupation[idown][1:7];
    (scfres.occupation[iup])[1:7] = [1.0, 0.9999987814392869, 0.9999987814392869, 0.9999987814392869, 0.9582253337797497, 0.9582253337797483, 1.1267168888941855e-29]
    +(scfres.occupation[idown])[1:7] = [1.0, 0.8438943064432616, 0.8438943064432515, 0.8438943064432596, 8.140906704021527e-6, 8.140906704021065e-6, 1.3999437398932187e-32]

    Similarly the eigenvalues differ

    @show scfres.eigenvalues[iup][1:7]
    +@show scfres.eigenvalues[idown][1:7];
    (scfres.eigenvalues[iup])[1:7] = [-0.06935855048019089, 0.3568856081149277, 0.35688560811492753, 0.35688560811492825, 0.4617360685346562, 0.4617360685346566, 1.1596205943303506]
    +(scfres.eigenvalues[idown])[1:7] = [-0.03125746719247491, 0.4761890577519535, 0.4761890577519543, 0.4761890577519537, 0.6102500058649546, 0.6102500058649551, 1.2265269065776894]
    ``k``-points in collinear calculations

    For collinear calculations the kpoints field of the PlaneWaveBasis object contains each $k$-point coordinate twice, once associated with spin-up and once with down-down. The list first contains all spin-up $k$-points and then all spin-down $k$-points, such that iup and idown index the same $k$-point, but differing spins.

    We can observe the spin-polarization by looking at the density of states (DOS) around the Fermi level, where the spin-up and spin-down DOS differ.

    using Plots
    +plot_dos(scfres)
    Example block output

    Similarly the band structure shows clear differences between both spin components.

    using Unitful
     using UnitfulAtomic
    -plot_bandstructure(scfres; kline_density=6)
    Example block output +plot_bandstructure(scfres; kline_density=6)Example block output diff --git a/dev/examples/compare_solvers.ipynb b/dev/examples/compare_solvers.ipynb index 8462847e4c..fb2247a5c1 100644 --- a/dev/examples/compare_solvers.ipynb +++ b/dev/examples/compare_solvers.ipynb @@ -70,16 +70,15 @@ "text": [ "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -7.846855428342 -0.70 4.2 \n", - " 2 -7.852295019513 -2.26 -1.53 1.0 17.4ms\n", - " 3 -7.852614284290 -3.50 -2.56 1.8 22.5ms\n", - " 4 -7.852645999064 -4.50 -2.90 2.5 34.8ms\n", - " 5 -7.852646522140 -6.28 -3.21 1.0 19.4ms\n", - " 6 -7.852646679691 -6.80 -4.11 1.0 17.6ms\n", - " 7 -7.852646686570 -8.16 -5.29 1.8 21.2ms\n", - " 8 -7.852646686724 -9.81 -5.42 1.8 20.9ms\n", - " 9 -7.852646686728 -11.41 -5.64 1.2 18.4ms\n", - " 10 -7.852646686730 -11.68 -6.58 1.0 18.2ms\n" + " 1 -7.846864154879 -0.70 5.0 \n", + " 2 -7.852321021414 -2.26 -1.53 1.0 33.4ms\n", + " 3 -7.852614986598 -3.53 -2.56 1.5 37.5ms\n", + " 4 -7.852646027121 -4.51 -2.90 2.5 46.5ms\n", + " 5 -7.852646525509 -6.30 -3.21 1.0 34.7ms\n", + " 6 -7.852646679051 -6.81 -4.01 1.0 34.0ms\n", + " 7 -7.852646686225 -8.14 -5.34 1.2 38.7ms\n", + " 8 -7.852646686725 -9.30 -5.50 3.0 51.2ms\n", + " 9 -7.852646686730 -11.31 -6.21 1.0 34.7ms\n" ] } ], @@ -105,14 +104,14 @@ "text": [ "n Energy log10(ΔE) log10(Δρ) α Diag Δtime\n", "--- --------------- --------- --------- ---- ---- ------\n", - " 1 -7.846871182472 -0.70 4.8 \n", - " 2 -7.852553401090 -2.25 -1.63 0.80 2.0 222ms\n", - " 3 -7.852639206153 -4.07 -2.73 0.80 1.0 16.4ms\n", - " 4 -7.852646582390 -5.13 -3.36 0.80 2.0 20.2ms\n", - " 5 -7.852646679166 -7.01 -4.32 0.80 1.2 16.8ms\n", - " 6 -7.852646686595 -8.13 -4.82 0.80 2.0 20.4ms\n", - " 7 -7.852646686719 -9.91 -5.55 0.80 1.2 17.2ms\n", - " 8 -7.852646686730 -10.97 -6.41 0.80 2.2 21.2ms\n" + " 1 -7.846811416770 -0.70 4.5 \n", + " 2 -7.852523999294 -2.24 -1.64 0.80 2.2 425ms\n", + " 3 -7.852635667477 -3.95 -2.74 0.80 1.0 32.3ms\n", + " 4 -7.852646496876 -4.97 -3.25 0.80 2.2 43.2ms\n", + " 5 -7.852646673484 -6.75 -4.06 0.80 1.2 34.8ms\n", + " 6 -7.852646686365 -7.89 -4.79 0.80 1.8 39.5ms\n", + " 7 -7.852646686724 -9.44 -5.63 0.80 1.8 42.8ms\n", + " 8 -7.852646686730 -11.24 -6.96 0.80 2.2 43.5ms\n" ] } ], @@ -139,90 +138,92 @@ "output_type": "stream", "text": [ "Iter Function value Gradient norm \n", - " 0 1.369147e+01 3.396804e+00\n", - " * time: 0.04660916328430176\n", - " 1 1.158552e+00 1.926141e+00\n", - " * time: 0.22212600708007812\n", - " 2 -1.510943e+00 2.308656e+00\n", - " * time: 0.2401731014251709\n", - " 3 -3.809331e+00 1.950597e+00\n", - " * time: 0.2659890651702881\n", - " 4 -4.959102e+00 1.843419e+00\n", - " * time: 0.2917470932006836\n", - " 5 -6.754448e+00 1.054595e+00\n", - " * time: 0.3176090717315674\n", - " 6 -7.402633e+00 6.594833e-01\n", - " * time: 0.3432772159576416\n", - " 7 -7.672100e+00 5.093120e-01\n", - " * time: 0.36113715171813965\n", - " 8 -7.760325e+00 1.641525e-01\n", - " * time: 0.3789820671081543\n", - " 9 -7.811002e+00 8.495859e-02\n", - " * time: 0.3969550132751465\n", - " 10 -7.834883e+00 8.032705e-02\n", - " * time: 0.4744691848754883\n", - " 11 -7.841936e+00 6.747633e-02\n", - " * time: 0.49250316619873047\n", - " 12 -7.845915e+00 3.531924e-02\n", - " * time: 0.5103771686553955\n", - " 13 -7.850068e+00 2.610939e-02\n", - " * time: 0.528205156326294\n", - " 14 -7.851708e+00 1.750772e-02\n", - " * time: 0.5459761619567871\n", - " 15 -7.852247e+00 1.134059e-02\n", - " * time: 0.563697099685669\n", - " 16 -7.852509e+00 7.868310e-03\n", - " * time: 0.581428050994873\n", - " 17 -7.852604e+00 6.497814e-03\n", - " * time: 0.5991220474243164\n", - " 18 -7.852632e+00 4.156133e-03\n", - " * time: 0.6169722080230713\n", - " 19 -7.852642e+00 1.233865e-03\n", - " * time: 0.6347780227661133\n", - " 20 -7.852645e+00 6.920639e-04\n", - " * time: 0.6528050899505615\n", - " 21 -7.852646e+00 5.098082e-04\n", - " * time: 0.6711971759796143\n", - " 22 -7.852647e+00 2.280875e-04\n", - " * time: 0.6889960765838623\n", - " 23 -7.852647e+00 1.331660e-04\n", - " * time: 0.7068459987640381\n", - " 24 -7.852647e+00 7.960506e-05\n", - " * time: 0.7248950004577637\n", - " 25 -7.852647e+00 6.216548e-05\n", - " * time: 0.7427201271057129\n", - " 26 -7.852647e+00 3.896591e-05\n", - " * time: 0.7607331275939941\n", - " 27 -7.852647e+00 1.929366e-05\n", - " * time: 0.7786362171173096\n", - " 28 -7.852647e+00 1.004750e-05\n", - " * time: 0.7966141700744629\n", - " 29 -7.852647e+00 4.648900e-06\n", - " * time: 0.8145341873168945\n", - " 30 -7.852647e+00 2.784603e-06\n", - " * time: 0.8323841094970703\n", - " 31 -7.852647e+00 1.519085e-06\n", - " * time: 0.8503081798553467\n", - " 32 -7.852647e+00 9.767333e-07\n", - " * time: 0.8681850433349609\n", - " 33 -7.852647e+00 7.518825e-07\n", - " * time: 0.8860650062561035\n", - " 34 -7.852647e+00 5.019369e-07\n", - " * time: 0.9039151668548584\n", - " 35 -7.852647e+00 3.028384e-07\n", - " * time: 0.9217700958251953\n", - " 36 -7.852647e+00 1.674703e-07\n", - " * time: 0.9398541450500488\n", - " 37 -7.852647e+00 8.029228e-08\n", - " * time: 0.9577422142028809\n", - " 38 -7.852647e+00 4.168666e-08\n", - " * time: 0.9755620956420898\n", - " 39 -7.852647e+00 2.311803e-08\n", - " * time: 0.9934110641479492\n", - " 40 -7.852647e+00 1.757029e-08\n", - " * time: 1.0118331909179688\n", - " 41 -7.852647e+00 1.757029e-08\n", - " * time: 1.108443021774292\n" + " 0 1.400390e+01 3.442336e+00\n", + " * time: 0.07780098915100098\n", + " 1 1.232377e+00 1.849758e+00\n", + " * time: 0.38660287857055664\n", + " 2 -1.308605e+00 2.251410e+00\n", + " * time: 0.421875\n", + " 3 -3.614169e+00 1.812183e+00\n", + " * time: 0.4751889705657959\n", + " 4 -4.899624e+00 1.750819e+00\n", + " * time: 0.5276849269866943\n", + " 5 -6.595659e+00 1.370181e+00\n", + " * time: 0.5798280239105225\n", + " 6 -7.331069e+00 6.429945e-01\n", + " * time: 0.6312530040740967\n", + " 7 -7.638945e+00 2.968303e-01\n", + " * time: 0.667639970779419\n", + " 8 -7.740198e+00 1.753960e-01\n", + " * time: 0.7030079364776611\n", + " 9 -7.792563e+00 1.590980e-01\n", + " * time: 0.7385590076446533\n", + " 10 -7.819281e+00 7.705658e-02\n", + " * time: 0.7745740413665771\n", + " 11 -7.836094e+00 6.813393e-02\n", + " * time: 0.8106980323791504\n", + " 12 -7.842581e+00 5.806714e-02\n", + " * time: 0.8463790416717529\n", + " 13 -7.845567e+00 3.909951e-02\n", + " * time: 0.8818018436431885\n", + " 14 -7.848593e+00 2.444701e-02\n", + " * time: 0.9173948764801025\n", + " 15 -7.851000e+00 2.113052e-02\n", + " * time: 0.9525468349456787\n", + " 16 -7.852108e+00 1.300558e-02\n", + " * time: 0.9883480072021484\n", + " 17 -7.852481e+00 6.110859e-03\n", + " * time: 1.0259950160980225\n", + " 18 -7.852592e+00 3.484142e-03\n", + " * time: 1.0618610382080078\n", + " 19 -7.852626e+00 1.963860e-03\n", + " * time: 1.097808837890625\n", + " 20 -7.852639e+00 1.286281e-03\n", + " * time: 1.1391949653625488\n", + " 21 -7.852645e+00 8.798988e-04\n", + " * time: 1.1751940250396729\n", + " 22 -7.852646e+00 5.880733e-04\n", + " * time: 1.2110099792480469\n", + " 23 -7.852647e+00 2.451330e-04\n", + " * time: 1.2560539245605469\n", + " 24 -7.852647e+00 1.357082e-04\n", + " * time: 1.296069860458374\n", + " 25 -7.852647e+00 9.123171e-05\n", + " * time: 1.3344659805297852\n", + " 26 -7.852647e+00 5.438304e-05\n", + " * time: 1.371377944946289\n", + " 27 -7.852647e+00 4.063436e-05\n", + " * time: 1.407071828842163\n", + " 28 -7.852647e+00 1.531698e-05\n", + " * time: 1.4419429302215576\n", + " 29 -7.852647e+00 1.094714e-05\n", + " * time: 1.47810697555542\n", + " 30 -7.852647e+00 6.719529e-06\n", + " * time: 1.5137250423431396\n", + " 31 -7.852647e+00 3.679616e-06\n", + " * time: 1.5479819774627686\n", + " 32 -7.852647e+00 2.283354e-06\n", + " * time: 1.5831868648529053\n", + " 33 -7.852647e+00 8.580341e-07\n", + " * time: 1.6187598705291748\n", + " 34 -7.852647e+00 5.652431e-07\n", + " * time: 1.653777837753296\n", + " 35 -7.852647e+00 2.969388e-07\n", + " * time: 1.6890718936920166\n", + " 36 -7.852647e+00 1.908662e-07\n", + " * time: 1.7244069576263428\n", + " 37 -7.852647e+00 1.686733e-07\n", + " * time: 1.7593519687652588\n", + " 38 -7.852647e+00 7.213846e-08\n", + " * time: 1.867616891860962\n", + " 39 -7.852647e+00 3.937412e-08\n", + " * time: 1.9029550552368164\n", + " 40 -7.852647e+00 2.145227e-08\n", + " * time: 1.9372270107269287\n", + " 41 -7.852647e+00 1.292835e-08\n", + " * time: 1.9717509746551514\n", + " 42 -7.852647e+00 6.337447e-09\n", + " * time: 2.0059549808502197\n" ] } ], @@ -256,7 +257,7 @@ "text": [ "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -7.846906936640 -0.70 4.5 \n" + " 1 -7.846699291812 -0.70 4.5 \n" ] } ], @@ -282,9 +283,9 @@ "text": [ "n Energy log10(ΔE) log10(Δρ) Δtime\n", "--- --------------- --------- --------- ------\n", - " 1 -7.852645906804 -1.65 \n", - " 2 -7.852646686730 -6.11 -3.72 1.50s\n", - " 3 -7.852646686730 -13.28 -7.23 119ms\n" + " 1 -7.852645739173 -1.64 \n", + " 2 -7.852646686730 -6.02 -3.70 2.67s\n", + " 3 -7.852646686730 -12.95 -7.11 262ms\n" ] } ], @@ -309,9 +310,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "|ρ_newton - ρ_scf| = 3.001457291571667e-7\n", - "|ρ_newton - ρ_scfv| = 3.476973564724203e-8\n", - "|ρ_newton - ρ_dm| = 4.559957502746101e-10\n" + "|ρ_newton - ρ_scf| = 8.032843069880328e-7\n", + "|ρ_newton - ρ_scfv| = 1.21564928605517e-7\n", + "|ρ_newton - ρ_dm| = 2.4322996018631903e-9\n" ] } ], diff --git a/dev/examples/compare_solvers/index.html b/dev/examples/compare_solvers/index.html index 094ddacf60..01a9658b6f 100644 --- a/dev/examples/compare_solvers/index.html +++ b/dev/examples/compare_solvers/index.html @@ -16,119 +16,121 @@ # Convergence we desire in the density tol = 1e-6
    1.0e-6

    Density-based self-consistent field

    scfres_scf = self_consistent_field(basis; tol);
    n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime
     ---   ---------------   ---------   ---------   ----   ------
    -  1   -7.846842201393                   -0.70    4.5
    -  2   -7.852321786024       -2.26       -1.53    1.0   16.8ms
    -  3   -7.852613935021       -3.53       -2.56    1.5   18.3ms
    -  4   -7.852645896519       -4.50       -2.88    2.5   22.5ms
    -  5   -7.852646479065       -6.23       -3.15    1.0   16.8ms
    -  6   -7.852646675659       -6.71       -3.93    1.0   16.8ms
    -  7   -7.852646686133       -7.98       -5.26    1.5   19.2ms
    -  8   -7.852646686708       -9.24       -5.11    3.0   24.5ms
    -  9   -7.852646686724      -10.79       -5.40    1.0   17.2ms
    - 10   -7.852646686729      -11.27       -6.17    1.0   17.5ms

    Potential-based SCF

    scfres_scfv = DFTK.scf_potential_mixing(basis; tol);
    n     Energy            log10(ΔE)   log10(Δρ)   α      Diag   Δtime
    +  1   -7.846767945346                   -0.71    4.5
    +  2   -7.852286024332       -2.26       -1.53    1.0   35.1ms
    +  3   -7.852614562372       -3.48       -2.56    1.8   39.9ms
    +  4   -7.852646041982       -4.50       -2.91    2.5   46.7ms
    +  5   -7.852646532816       -6.31       -3.23    1.0   33.5ms
    +  6   -7.852646679742       -6.83       -4.10    1.0   34.3ms
    +  7   -7.852646686694       -8.16       -5.16    2.2   45.1ms
    +  8   -7.852646686728      -10.47       -5.68    1.5   38.8ms
    +  9   -7.852646686728      -12.51       -5.61    1.5   38.2ms
    + 10   -7.852646686730      -11.75       -6.86    1.0   34.8ms

    Potential-based SCF

    scfres_scfv = DFTK.scf_potential_mixing(basis; tol);
    n     Energy            log10(ΔE)   log10(Δρ)   α      Diag   Δtime
     ---   ---------------   ---------   ---------   ----   ----   ------
    -  1   -7.846871077770                   -0.70           4.8
    -  2   -7.852527543017       -2.25       -1.63   0.80    2.0   19.4ms
    -  3   -7.852636719323       -3.96       -2.72   0.80    1.0   15.6ms
    -  4   -7.852646465525       -5.01       -3.29   0.80    2.2   20.7ms
    -  5   -7.852646678003       -6.67       -4.10   0.80    1.2   16.9ms
    -  6   -7.852646686321       -8.08       -4.75   0.80    1.5   17.8ms
    -  7   -7.852646686724       -9.39       -5.71   0.80    1.5   18.3ms
    -  8   -7.852646686730      -11.25       -6.92   0.80    2.5   20.6ms

    Direct minimization

    Note: Unlike the other algorithms, tolerance for this one is in the energy, thus we square the density tolerance value to be roughly equivalent.

    scfres_dm = direct_minimization(basis; tol=tol^2);
    Iter     Function value   Gradient norm
    -     0     1.380206e+01     3.474329e+00
    - * time: 0.009474992752075195
    -     1     1.492870e+00     1.917928e+00
    - * time: 0.027178049087524414
    -     2    -1.430882e+00     2.253712e+00
    - * time: 0.04490494728088379
    -     3    -3.761484e+00     1.862380e+00
    - * time: 0.07050204277038574
    -     4    -5.094671e+00     1.599948e+00
    - * time: 0.09600186347961426
    -     5    -6.889386e+00     9.772694e-01
    - * time: 0.12151002883911133
    -     6    -7.528024e+00     2.186593e-01
    - * time: 0.14706087112426758
    -     7    -7.680799e+00     1.357563e-01
    - * time: 0.16471505165100098
    -     8    -7.760999e+00     8.753248e-02
    - * time: 0.18230605125427246
    -     9    -7.786869e+00     8.579462e-02
    - * time: 0.19998788833618164
    -    10    -7.802100e+00     8.345342e-02
    - * time: 0.21767091751098633
    -    11    -7.816393e+00     7.753517e-02
    - * time: 0.2354140281677246
    -    12    -7.835085e+00     6.290448e-02
    - * time: 0.2531120777130127
    -    13    -7.846505e+00     3.572346e-02
    - * time: 0.27078795433044434
    -    14    -7.850650e+00     2.433022e-02
    - * time: 0.28845691680908203
    -    15    -7.851882e+00     1.988834e-02
    - * time: 0.30614590644836426
    -    16    -7.852434e+00     7.900060e-03
    - * time: 0.32382702827453613
    -    17    -7.852586e+00     3.582378e-03
    - * time: 0.34151196479797363
    -    18    -7.852628e+00     1.923475e-03
    - * time: 0.3781719207763672
    -    19    -7.852640e+00     1.110088e-03
    - * time: 0.39636898040771484
    -    20    -7.852645e+00     8.452605e-04
    - * time: 0.4142749309539795
    -    21    -7.852646e+00     5.113261e-04
    - * time: 0.43198299407958984
    -    22    -7.852646e+00     3.628213e-04
    - * time: 0.44967007637023926
    -    23    -7.852647e+00     1.720097e-04
    - * time: 0.46733903884887695
    -    24    -7.852647e+00     7.747890e-05
    - * time: 0.4850149154663086
    -    25    -7.852647e+00     4.793400e-05
    - * time: 0.5026159286499023
    -    26    -7.852647e+00     2.813315e-05
    - * time: 0.5201780796051025
    -    27    -7.852647e+00     1.649968e-05
    - * time: 0.537775993347168
    -    28    -7.852647e+00     1.055827e-05
    - * time: 0.5554120540618896
    -    29    -7.852647e+00     5.980045e-06
    - * time: 0.5730929374694824
    -    30    -7.852647e+00     3.274511e-06
    - * time: 0.5907449722290039
    -    31    -7.852647e+00     2.486999e-06
    - * time: 0.6083810329437256
    -    32    -7.852647e+00     1.241727e-06
    - * time: 0.6260449886322021
    -    33    -7.852647e+00     4.362670e-07
    - * time: 0.6437060832977295
    -    34    -7.852647e+00     2.799429e-07
    - * time: 0.6613800525665283
    -    35    -7.852647e+00     2.494465e-07
    - * time: 0.6789648532867432
    -    36    -7.852647e+00     1.483884e-07
    - * time: 0.6965739727020264
    -    37    -7.852647e+00     6.631125e-08
    - * time: 0.7142140865325928
    -    38    -7.852647e+00     2.886716e-08
    - * time: 0.7318649291992188
    -    39    -7.852647e+00     2.181637e-08
    - * time: 0.7495789527893066
    -    40    -7.852647e+00     2.147421e-08
    - * time: 0.7909250259399414
    -    41    -7.852647e+00     1.221305e-08
    - * time: 0.8086750507354736
    -    42    -7.852647e+00     1.221305e-08
    - * time: 0.9143900871276855

    Newton algorithm

    Start not too far from the solution to ensure convergence: We run first a very crude SCF to get close and then switch to Newton.

    scfres_start = self_consistent_field(basis; tol=0.5);
    n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime
    +  1   -7.846832014112                   -0.70           4.5
    +  2   -7.852551183323       -2.24       -1.63   0.80    2.2   41.8ms
    +  3   -7.852638581081       -4.06       -2.74   0.80    1.0   31.9ms
    +  4   -7.852646568549       -5.10       -3.34   0.80    2.0   40.4ms
    +  5   -7.852646677819       -6.96       -4.26   0.80    1.5   35.6ms
    +  6   -7.852646686583       -8.06       -4.82   0.80    2.2   41.6ms
    +  7   -7.852646686719       -9.87       -5.62   0.80    1.2   33.2ms
    +  8   -7.852646686730      -10.98       -6.67   0.80    2.2   42.2ms

    Direct minimization

    Note: Unlike the other algorithms, tolerance for this one is in the energy, thus we square the density tolerance value to be roughly equivalent.

    scfres_dm = direct_minimization(basis; tol=tol^2);
    Iter     Function value   Gradient norm
    +     0     1.349687e+01     3.777990e+00
    + * time: 0.018877029418945312
    +     1     1.380127e+00     1.744276e+00
    + * time: 0.05246400833129883
    +     2    -1.781112e+00     1.898896e+00
    + * time: 0.08592414855957031
    +     3    -3.796766e+00     1.827160e+00
    + * time: 0.13683009147644043
    +     4    -5.351278e+00     1.930943e+00
    + * time: 0.19168615341186523
    +     5    -6.929057e+00     1.296355e+00
    + * time: 0.24034905433654785
    +     6    -7.136206e+00     1.576331e+00
    + * time: 0.2737760543823242
    +     7    -7.607813e+00     8.377548e-01
    + * time: 0.30707812309265137
    +     8    -7.689789e+00     1.266391e+00
    + * time: 0.3404369354248047
    +     9    -7.723122e+00     9.826713e-01
    + * time: 0.37583208084106445
    +    10    -7.762623e+00     4.888249e-01
    + * time: 0.429124116897583
    +    11    -7.787322e+00     6.808502e-01
    + * time: 0.46437907218933105
    +    12    -7.809965e+00     1.537583e-01
    + * time: 0.5369820594787598
    +    13    -7.822022e+00     1.095211e-01
    + * time: 0.57169508934021
    +    14    -7.838166e+00     8.990275e-02
    + * time: 0.6048779487609863
    +    15    -7.847653e+00     8.254102e-02
    + * time: 0.6390490531921387
    +    16    -7.851026e+00     1.688621e-02
    + * time: 0.6740410327911377
    +    17    -7.852102e+00     1.510591e-02
    + * time: 0.7075841426849365
    +    18    -7.852482e+00     1.157039e-02
    + * time: 0.7411229610443115
    +    19    -7.852602e+00     4.491363e-03
    + * time: 0.7750661373138428
    +    20    -7.852628e+00     4.647319e-03
    + * time: 0.8087880611419678
    +    21    -7.852639e+00     2.070564e-03
    + * time: 0.8433749675750732
    +    22    -7.852644e+00     9.002429e-04
    + * time: 0.8778929710388184
    +    23    -7.852646e+00     6.131367e-04
    + * time: 0.9112980365753174
    +    24    -7.852646e+00     2.825461e-04
    + * time: 0.9451980590820312
    +    25    -7.852647e+00     2.024298e-04
    + * time: 0.9796700477600098
    +    26    -7.852647e+00     9.665635e-05
    + * time: 1.0125269889831543
    +    27    -7.852647e+00     5.175352e-05
    + * time: 1.046375036239624
    +    28    -7.852647e+00     3.945531e-05
    + * time: 1.0805790424346924
    +    29    -7.852647e+00     2.375218e-05
    + * time: 1.115312099456787
    +    30    -7.852647e+00     1.726208e-05
    + * time: 1.1494081020355225
    +    31    -7.852647e+00     8.683770e-06
    + * time: 1.1846680641174316
    +    32    -7.852647e+00     6.272200e-06
    + * time: 1.222546100616455
    +    33    -7.852647e+00     4.127579e-06
    + * time: 1.2574760913848877
    +    34    -7.852647e+00     2.655722e-06
    + * time: 1.2913479804992676
    +    35    -7.852647e+00     6.321755e-07
    + * time: 1.325991153717041
    +    36    -7.852647e+00     4.329478e-07
    + * time: 1.3603100776672363
    +    37    -7.852647e+00     3.704188e-07
    + * time: 1.3939769268035889
    +    38    -7.852647e+00     1.602392e-07
    + * time: 1.4301609992980957
    +    39    -7.852647e+00     1.166750e-07
    + * time: 1.4663770198822021
    +    40    -7.852647e+00     5.737597e-08
    + * time: 1.4999361038208008
    +    41    -7.852647e+00     4.665537e-08
    + * time: 1.5358381271362305
    +    42    -7.852647e+00     1.906072e-08
    + * time: 1.5694289207458496
    +    43    -7.852647e+00     1.226862e-08
    + * time: 1.6068661212921143

    Newton algorithm

    Start not too far from the solution to ensure convergence: We run first a very crude SCF to get close and then switch to Newton.

    scfres_start = self_consistent_field(basis; tol=0.5);
    n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime
     ---   ---------------   ---------   ---------   ----   ------
    -  1   -7.846806285683                   -0.70    4.5

    Remove the virtual orbitals (which Newton cannot treat yet)

    ψ = DFTK.select_occupied_orbitals(basis, scfres_start.ψ, scfres_start.occupation).ψ
    +  1   -7.846812927765                   -0.70    4.5

    Remove the virtual orbitals (which Newton cannot treat yet)

    ψ = DFTK.select_occupied_orbitals(basis, scfres_start.ψ, scfres_start.occupation).ψ
     scfres_newton = newton(basis, ψ; tol);
    n     Energy            log10(ΔE)   log10(Δρ)   Δtime
     ---   ---------------   ---------   ---------   ------
    -  1   -7.852645864520                   -1.64
    -  2   -7.852646686730       -6.09       -3.70    314ms
    -  3   -7.852646686730      -13.21       -7.21    121ms

    Comparison of results

    println("|ρ_newton - ρ_scf|  = ", norm(scfres_newton.ρ - scfres_scf.ρ))
    +  1   -7.852645883546                   -1.64
    +  2   -7.852646686730       -6.10       -3.70    673ms
    +  3   -7.852646686730      -13.26       -7.23    274ms

    Comparison of results

    println("|ρ_newton - ρ_scf|  = ", norm(scfres_newton.ρ - scfres_scf.ρ))
     println("|ρ_newton - ρ_scfv| = ", norm(scfres_newton.ρ - scfres_scfv.ρ))
    -println("|ρ_newton - ρ_dm|   = ", norm(scfres_newton.ρ - scfres_dm.ρ))
    |ρ_newton - ρ_scf|  = 5.395262390295145e-7
    -|ρ_newton - ρ_scfv| = 1.6980073272389236e-7
    -|ρ_newton - ρ_dm|   = 9.821508384157374e-10
    +println("|ρ_newton - ρ_dm| = ", norm(scfres_newton.ρ - scfres_dm.ρ))
    |ρ_newton - ρ_scf|  = 2.301406060208815e-7
    +|ρ_newton - ρ_scfv| = 5.142119127904244e-8
    +|ρ_newton - ρ_dm|   = 1.0024054984750926e-9
    diff --git a/dev/examples/convergence_study.ipynb b/dev/examples/convergence_study.ipynb index c637cb1cc5..8244f5fccf 100644 --- a/dev/examples/convergence_study.ipynb +++ b/dev/examples/convergence_study.ipynb @@ -87,52 +87,52 @@ "nkpt = 1 Ecut = 17.0\n", "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -26.49825841137 -0.22 8.0 \n", - " 2 -26.59126726077 -1.03 -0.63 2.0 52.9ms\n", - " 3 -26.61283291898 -1.67 -1.40 2.0 24.8ms\n", - " 4 -26.61326792660 -3.36 -2.09 2.0 25.3ms\n", + " 1 -26.49793828876 -0.22 8.0 \n", + " 2 -26.59001146300 -1.04 -0.62 2.0 84.7ms\n", + " 3 -26.61287969996 -1.64 -1.40 2.0 38.8ms\n", + " 4 -26.61325874079 -3.42 -2.07 2.0 43.1ms\n", "nkpt = 2 Ecut = 17.0\n", "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -25.79142189189 -0.09 7.0 \n", - " 2 -26.23199703965 -0.36 -0.70 2.0 91.4ms\n", - " 3 -26.23819253819 -2.21 -1.32 2.0 110ms\n", - " 4 -26.23847886488 -3.54 -2.33 1.0 69.3ms\n", + " 1 -25.79185667843 -0.09 7.2 \n", + " 2 -26.23198260430 -0.36 -0.70 2.0 163ms\n", + " 3 -26.23819055424 -2.21 -1.32 2.0 165ms\n", + " 4 -26.23847847470 -3.54 -2.33 1.0 116ms\n", "nkpt = 3 Ecut = 17.0\n", "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -25.78520088491 -0.09 5.0 \n", - " 2 -26.23814223621 -0.34 -0.78 2.0 92.3ms\n", - " 3 -26.25072756643 -1.90 -1.63 2.0 96.8ms\n", - " 4 -26.25103528192 -3.51 -2.15 1.2 84.9ms\n", + " 1 -25.78492018324 -0.09 4.8 \n", + " 2 -26.23816905702 -0.34 -0.78 2.0 146ms\n", + " 3 -26.25073154716 -1.90 -1.63 2.0 145ms\n", + " 4 -26.25103573728 -3.52 -2.15 1.2 126ms\n", "nkpt = 4 Ecut = 17.0\n", "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -25.91132353635 -0.11 5.4 \n", - " 2 -26.29254629871 -0.42 -0.76 1.7 160ms\n", - " 3 -26.30833988799 -1.80 -1.73 2.3 204ms\n", - " 4 -26.30842590403 -4.07 -2.70 1.1 128ms\n", + " 1 -25.91142099241 -0.11 5.4 \n", + " 2 -26.29265400590 -0.42 -0.76 1.8 250ms\n", + " 3 -26.30834330481 -1.80 -1.74 2.2 294ms\n", + " 4 -26.30842631446 -4.08 -2.72 1.1 218ms\n", "nkpt = 5 Ecut = 17.0\n", "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -25.90217886121 -0.11 3.8 \n", - " 2 -26.26461197622 -0.44 -0.71 1.9 158ms\n", - " 3 -26.28541834437 -1.68 -1.61 2.1 197ms\n", - " 4 -26.28570513288 -3.54 -2.23 1.8 161ms\n", + " 1 -25.90248914526 -0.11 3.9 \n", + " 2 -26.26446651607 -0.44 -0.71 1.9 265ms\n", + " 3 -26.28540922379 -1.68 -1.61 2.1 280ms\n", + " 4 -26.28570401232 -3.53 -2.22 1.9 261ms\n", "nkpt = 6 Ecut = 17.0\n", "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -25.87461378855 -0.10 5.2 \n", - " 2 -26.27154487802 -0.40 -0.75 1.8 290ms\n", - " 3 -26.28806197364 -1.78 -1.69 2.1 364ms\n", - " 4 -26.28818589584 -3.91 -2.57 1.1 234ms\n", + " 1 -25.87482664422 -0.10 4.9 \n", + " 2 -26.27171085850 -0.40 -0.76 1.9 455ms\n", + " 3 -26.28806785801 -1.79 -1.69 2.0 516ms\n", + " 4 -26.28818664176 -3.93 -2.61 1.1 378ms\n", "nkpt = 7 Ecut = 17.0\n", "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -25.89693448902 -0.11 3.7 \n", - " 2 -26.27704767983 -0.42 -0.74 1.9 336ms\n", - " 3 -26.29414589637 -1.77 -1.73 2.0 382ms\n", - " 4 -26.29420699372 -4.21 -2.69 1.0 222ms\n" + " 1 -25.89695544479 -0.11 3.5 \n", + " 2 -26.27691951479 -0.42 -0.74 1.9 497ms\n", + " 3 -26.29414086687 -1.76 -1.73 2.0 547ms\n", + " 4 -26.29420678145 -4.18 -2.68 1.0 363ms\n" ] }, { @@ -171,108 +171,108 @@ "output_type": "execute_result", "data": { "text/plain": "Plot{Plots.GRBackend() n=1}", - "image/png": 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------\n", - " 1 -25.78653966169 -0.12 3.5 \n", - " 2 -26.07620505559 -0.54 -0.71 1.7 69.6ms\n", - " 3 -26.09344527457 -1.76 -1.69 2.0 94.6ms\n", - " 4 -26.09374141766 -3.53 -2.33 1.5 65.8ms\n", - " 5 -26.09375430452 -4.89 -2.73 1.9 77.6ms\n", + " 1 -25.78677831998 -0.12 3.5 \n", + " 2 -26.07614460606 -0.54 -0.71 1.8 134ms\n", + " 3 -26.09343907784 -1.76 -1.69 2.0 151ms\n", + " 4 -26.09374055720 -3.52 -2.32 1.4 152ms\n", + " 5 -26.09375408471 -4.87 -2.72 2.0 145ms\n", "nkpt = 5 Ecut = 14\n", "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -25.86777316672 -0.11 3.5 \n", - " 2 -26.20704820144 -0.47 -0.71 1.9 60.4ms\n", - " 3 -26.22669352133 -1.71 -1.64 2.0 75.9ms\n", - " 4 -26.22700250466 -3.51 -2.25 1.7 61.0ms\n", - " 5 -26.22702646775 -4.62 -2.73 1.7 63.2ms\n", + " 1 -25.86800340062 -0.11 3.5 \n", + " 2 -26.20703452213 -0.47 -0.71 2.0 110ms\n", + " 3 -26.22669529931 -1.71 -1.64 2.0 120ms\n", + " 4 -26.22700304692 -3.51 -2.25 1.7 117ms\n", + " 5 -26.22702626074 -4.63 -2.72 1.9 115ms\n", "nkpt = 5 Ecut = 16\n", "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -25.89685395205 -0.11 3.7 \n", - " 2 -26.25499051559 -0.45 -0.71 2.0 154ms\n", - " 3 -26.27557953843 -1.69 -1.62 2.1 196ms\n", - " 4 -26.27586861117 -3.54 -2.24 1.9 156ms\n", - " 5 -26.27589342031 -4.61 -2.77 1.7 165ms\n", + " 1 -25.89685185578 -0.11 3.8 \n", + " 2 -26.25467501311 -0.45 -0.71 1.8 254ms\n", + " 3 -26.27556023386 -1.68 -1.61 2.1 284ms\n", + " 4 -26.27586609353 -3.51 -2.22 2.0 264ms\n", + " 5 -26.27589348161 -4.56 -2.77 1.9 257ms\n", "nkpt = 5 Ecut = 18\n", "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -25.90517394442 -0.11 4.5 \n", - " 2 -26.27021315663 -0.44 -0.71 2.0 163ms\n", - " 3 -26.29100097397 -1.68 -1.62 2.2 203ms\n", - " 4 -26.29127902947 -3.56 -2.23 1.7 154ms\n", - " 5 -26.29130421293 -4.60 -2.76 1.8 178ms\n", + " 1 -25.90513746306 -0.11 4.4 \n", + " 2 -26.27012376976 -0.44 -0.71 2.0 262ms\n", + " 3 -26.29099427659 -1.68 -1.61 2.2 285ms\n", + " 4 -26.29127839234 -3.55 -2.23 1.8 259ms\n", + " 5 -26.29130428463 -4.59 -2.76 2.0 280ms\n", "nkpt = 5 Ecut = 20\n", "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -25.90784934350 -0.11 3.9 \n", - " 2 -26.27444284076 -0.44 -0.71 1.9 166ms\n", - " 3 -26.29529381601 -1.68 -1.61 2.2 211ms\n", - " 4 -26.29557805166 -3.55 -2.21 1.9 172ms\n", - " 5 -26.29560602244 -4.55 -2.77 1.9 188ms\n", + " 1 -25.90776059439 -0.11 3.8 \n", + " 2 -26.27436815821 -0.44 -0.71 1.9 261ms\n", + " 3 -26.29529495308 -1.68 -1.61 2.3 306ms\n", + " 4 -26.29557788555 -3.55 -2.21 1.7 250ms\n", + " 5 -26.29560599565 -4.55 -2.77 2.0 290ms\n", "nkpt = 5 Ecut = 22\n", "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -25.90838432963 -0.11 3.9 \n", - " 2 -26.27579101317 -0.43 -0.71 2.0 180ms\n", - " 3 -26.29615670517 -1.69 -1.63 2.1 202ms\n", - " 4 -26.29641443760 -3.59 -2.25 1.9 183ms\n", - " 5 -26.29643550531 -4.68 -2.74 1.9 187ms\n", + " 1 -25.90843226280 -0.11 3.9 \n", + " 2 -26.27546241688 -0.44 -0.71 2.0 281ms\n", + " 3 -26.29614552958 -1.68 -1.62 2.2 303ms\n", + " 4 -26.29641209093 -3.57 -2.24 1.7 283ms\n", + " 5 -26.29643560888 -4.63 -2.76 1.9 272ms\n", "nkpt = 5 Ecut = 24\n", "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -25.90877355698 -0.11 3.8 \n", - " 2 -26.27549106230 -0.44 -0.71 2.0 171ms\n", - " 3 -26.29620963030 -1.68 -1.62 2.2 195ms\n", - " 4 -26.29648674365 -3.56 -2.22 2.0 177ms\n", - " 5 -26.29651152767 -4.61 -2.76 1.9 177ms\n" + " 1 -25.90856010852 -0.11 4.0 \n", + " 2 -26.27594043262 -0.43 -0.71 2.0 240ms\n", + " 3 -26.29623805229 -1.69 -1.63 2.2 270ms\n", + " 4 -26.29649179998 -3.60 -2.25 2.0 239ms\n", + " 5 -26.29651162258 -4.70 -2.75 1.9 252ms\n" ] }, { @@ -402,106 +402,106 @@ "output_type": "execute_result", "data": { "text/plain": "Plot{Plots.GRBackend() n=1}", - "image/png": 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diff --git a/dev/examples/convergence_study/927ca7d0.svg b/dev/examples/convergence_study/927ca7d0.svg deleted file mode 100644 index c45ce6cdac..0000000000 --- a/dev/examples/convergence_study/927ca7d0.svg +++ /dev/null @@ -1,49 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/dev/examples/convergence_study/ffb680c8.svg b/dev/examples/convergence_study/ffb680c8.svg deleted file mode 100644 index e505ead729..0000000000 --- a/dev/examples/convergence_study/ffb680c8.svg +++ /dev/null @@ -1,48 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/dev/examples/convergence_study/index.html b/dev/examples/convergence_study/index.html index 859562547f..e75ce201a6 100644 --- a/dev/examples/convergence_study/index.html +++ b/dev/examples/convergence_study/index.html @@ -23,7 +23,7 @@ result = converge_kgrid(nkpts; Ecut=mean(Ecuts), tol) nkpt_conv = result.nkpt_conv
    5

    … and plot the obtained convergence:

    using Plots
     plot(result.nkpts, result.errors, dpi=300, lw=3, m=:o, yaxis=:log,
    -     xlabel="k-grid", ylabel="energy absolute error")
    Example block output

    We continue to do the convergence in Ecut using the suggested $k$-point grid.

    function converge_Ecut(Ecuts; nkpt, tol)
    +     xlabel="k-grid", ylabel="energy absolute error")
    Example block output

    We continue to do the convergence in Ecut using the suggested $k$-point grid.

    function converge_Ecut(Ecuts; nkpt, tol)
         energies = [run_scf(; nkpt, tol=tol/100, Ecut).energies.total for Ecut in Ecuts]
         errors = abs.(energies[1:end-1] .- energies[end])
         iconv = findfirst(errors .< tol)
    @@ -31,7 +31,7 @@
     end
     result = converge_Ecut(Ecuts; nkpt=nkpt_conv, tol)
     Ecut_conv = result.Ecut_conv
    18

    … and plot it:

    plot(result.Ecuts, result.errors, dpi=300, lw=3, m=:o, yaxis=:log,
    -     xlabel="Ecut", ylabel="energy absolute error")
    Example block output

    A more realistic example.

    Repeating the above exercise for more realistic settings, namely …

    tol   = 1e-4  # Tolerance to which we target to converge
    +     xlabel="Ecut", ylabel="energy absolute error")
    Example block output

    A more realistic example.

    Repeating the above exercise for more realistic settings, namely …

    tol   = 1e-4  # Tolerance to which we target to converge
     nkpts = 1:20  # K-point range checked for convergence
     Ecuts = 20:1:50;

    …one obtains the following two plots for the convergence in kpoints and Ecut.

    - + diff --git a/dev/examples/custom_potential.ipynb b/dev/examples/custom_potential.ipynb index 785893610d..dce694dc34 100644 --- a/dev/examples/custom_potential.ipynb +++ b/dev/examples/custom_potential.ipynb @@ -177,20 +177,21 @@ "text": [ "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -0.143615320908 -0.42 7.0 \n", - " 2 -0.156039008938 -1.91 -1.10 1.0 1.21ms\n", - " 3 -0.156771609764 -3.14 -1.57 1.0 919μs\n", - " 4 -0.157043068826 -3.57 -2.30 1.0 874μs\n", - " 5 -0.157034847669 + -5.09 -2.29 2.0 1.23ms\n", - " 6 -0.157056389623 -4.67 -3.67 1.0 875μs\n", - " 7 -0.157056405663 -7.79 -4.20 1.0 881μs\n", - " 8 -0.157056406886 -8.91 -5.02 1.0 887μs\n" + " 1 -0.143684429751 -0.42 7.0 \n", + " 2 -0.156024700218 -1.91 -1.10 1.0 2.01ms\n", + " 3 -0.156765418423 -3.13 -1.56 1.0 1.77ms\n", + " 4 -0.157046434086 -3.55 -2.33 1.0 1.78ms\n", + " 5 -0.157055178336 -5.06 -2.91 1.0 1.78ms\n", + " 6 -0.157056386337 -5.92 -3.62 1.0 1.91ms\n", + " 7 -0.157056406274 -7.70 -4.30 1.0 1.74ms\n", + " 8 -0.157056406853 -9.24 -4.88 1.0 1.67ms\n", + " 9 -0.157056406917 -10.19 -5.84 1.0 1.91ms\n" ] }, { "output_type": "execute_result", "data": { - "text/plain": "Energy breakdown (in Ha):\n Kinetic 0.0380287 \n AtomicLocal -0.3163450\n LocalNonlinearity 0.1212599 \n\n total -0.157056406886" + "text/plain": "Energy breakdown (in Ha):\n Kinetic 0.0380295 \n AtomicLocal -0.3163466\n LocalNonlinearity 0.1212607 \n\n total -0.157056406917" }, "metadata": {}, "execution_count": 8 @@ -218,7 +219,7 @@ { "output_type": "execute_result", "data": { - "text/plain": "2-element Vector{StaticArraysCore.SVector{3, Float64}}:\n [-0.05567316937543834, 0.0, 0.0]\n [0.055663148864749365, 0.0, 0.0]" + "text/plain": "2-element Vector{StaticArraysCore.SVector{3, Float64}}:\n [-0.05568140351008649, 0.0, 0.0]\n [0.05568171011144929, 0.0, 0.0]" }, "metadata": {}, "execution_count": 9 @@ -260,114 +261,114 @@ "output_type": "execute_result", "data": { "text/plain": "Plot{Plots.GRBackend() n=4}", - "image/png": 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", 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a/dev/examples/custom_potential/e6240b6a.svg +++ /dev/null @@ -1,52 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/dev/examples/custom_potential/index.html b/dev/examples/custom_potential/index.html index 910b75245e..198c19461b 100644 --- a/dev/examples/custom_potential/index.html +++ b/dev/examples/custom_potential/index.html @@ -25,13 +25,13 @@ ρ = zeros(eltype(basis), basis.fft_size..., 1) scfres = self_consistent_field(basis; tol=1e-5, ρ) scfres.energies
    Energy breakdown (in Ha):
    -    Kinetic             0.0380292 
    -    AtomicLocal         -0.3163462
    +    Kinetic             0.0380293 
    +    AtomicLocal         -0.3163463
         LocalNonlinearity   0.1212605 
     
    -    total               -0.157056406915

    Computing the forces can then be done as usual:

    compute_forces(scfres)
    2-element Vector{StaticArraysCore.SVector{3, Float64}}:
    - [-0.055679219037558736, 0.0, 0.0]
    - [0.055681239989680956, 0.0, 0.0]

    Extract the converged total local potential

    tot_local_pot = DFTK.total_local_potential(scfres.ham)[:, 1, 1]; # use only dimension 1

    Extract other quantities before plotting them

    ρ = scfres.ρ[:, 1, 1, 1]        # converged density, first spin component
    +    total               -0.157056406917

    Computing the forces can then be done as usual:

    compute_forces(scfres)
    2-element Vector{StaticArraysCore.SVector{3, Float64}}:
    + [-0.055679783882304754, 0.0, 0.0]
    + [0.05568147027085321, 0.0, 0.0]

    Extract the converged total local potential

    tot_local_pot = DFTK.total_local_potential(scfres.ham)[:, 1, 1]; # use only dimension 1

    Extract other quantities before plotting them

    ρ = scfres.ρ[:, 1, 1, 1]        # converged density, first spin component
     ψ_fourier = scfres.ψ[1][:, 1]   # first k-point, all G components, first eigenvector
     ψ = ifft(basis, basis.kpoints[1], ψ_fourier)[:, 1, 1]
     ψ /= (ψ[div(end, 2)] / abs(ψ[div(end, 2)]));
    @@ -41,4 +41,4 @@
     p = plot(x, real.(ψ), label="real(ψ)")
     plot!(p, x, imag.(ψ), label="imag(ψ)")
     plot!(p, x, ρ, label="ρ")
    -plot!(p, x, tot_local_pot, label="tot local pot")
    Example block output +plot!(p, x, tot_local_pot, label="tot local pot")Example block output diff --git a/dev/examples/custom_solvers.ipynb b/dev/examples/custom_solvers.ipynb index fe86326dbb..2c23e5dcf5 100644 --- a/dev/examples/custom_solvers.ipynb +++ b/dev/examples/custom_solvers.ipynb @@ -131,20 +131,19 @@ "text": [ "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -7.136730769250 -0.42 0.0 \n", - " 2 -7.234507523075 -1.01 -0.71 0.0 331ms\n", - " 3 -7.250155356676 -1.81 -1.20 0.0 135ms\n", - " 4 -7.251066644663 -3.04 -1.51 0.0 50.3ms\n", - " 5 -7.251276232104 -3.68 -1.82 0.0 49.6ms\n", - " 6 -7.251323976978 -4.32 -2.11 0.0 51.9ms\n", - " 7 -7.251335118065 -4.95 -2.39 0.0 53.8ms\n", - " 8 -7.251337831814 -5.57 -2.66 0.0 53.6ms\n", - " 9 -7.251338529334 -6.16 -2.92 0.0 142ms\n", - " 10 -7.251338719368 -6.72 -3.18 0.0 49.9ms\n", - " 11 -7.251338774177 -7.26 -3.44 0.0 50.4ms\n", - " 12 -7.251338790816 -7.78 -3.68 0.0 52.8ms\n", - " 13 -7.251338796088 -8.28 -3.93 0.0 53.0ms\n", - " 14 -7.251338797816 -8.76 -4.17 0.0 141ms\n" + " 1 -7.212054275736 -0.48 0.0 \n", + " 2 -7.245780349388 -1.47 -0.85 0.0 591ms\n", + " 3 -7.250946569942 -2.29 -1.30 0.0 94.4ms\n", + " 4 -7.251245395197 -3.52 -1.61 0.0 96.8ms\n", + " 5 -7.251316073962 -4.15 -1.92 0.0 218ms\n", + " 6 -7.251333026616 -4.77 -2.21 0.0 91.3ms\n", + " 7 -7.251337250559 -5.37 -2.50 0.0 94.1ms\n", + " 8 -7.251338358710 -5.96 -2.78 0.0 93.1ms\n", + " 9 -7.251338666610 -6.51 -3.05 0.0 94.9ms\n", + " 10 -7.251338757129 -7.04 -3.31 0.0 96.8ms\n", + " 11 -7.251338785114 -7.55 -3.56 0.0 216ms\n", + " 12 -7.251338794133 -8.04 -3.81 0.0 93.3ms\n", + " 13 -7.251338797134 -8.52 -4.05 0.0 97.9ms\n" ] } ], diff --git a/dev/examples/custom_solvers/index.html b/dev/examples/custom_solvers/index.html index fb6afd25f7..4819513495 100644 --- a/dev/examples/custom_solvers/index.html +++ b/dev/examples/custom_solvers/index.html @@ -49,17 +49,16 @@ eigensolver=my_eig_solver, mixing=MyMixing());
    n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime
     ---   ---------------   ---------   ---------   ----   ------
    -  1   -7.136730769250                   -0.42    0.0
    -  2   -7.234507523075       -1.01       -0.71    0.0    286ms
    -  3   -7.250155356676       -1.81       -1.20    0.0   51.3ms
    -  4   -7.251066644663       -3.04       -1.51    0.0    136ms
    -  5   -7.251276232104       -3.68       -1.82    0.0   49.4ms
    -  6   -7.251323976978       -4.32       -2.11    0.0   51.8ms
    -  7   -7.251335118065       -4.95       -2.39    0.0   51.6ms
    -  8   -7.251337831814       -5.57       -2.66    0.0   47.6ms
    -  9   -7.251338529334       -6.16       -2.92    0.0    137ms
    - 10   -7.251338719368       -6.72       -3.18    0.0   39.2ms
    - 11   -7.251338774177       -7.26       -3.44    0.0   48.1ms
    - 12   -7.251338790816       -7.78       -3.68    0.0   59.2ms
    - 13   -7.251338796088       -8.28       -3.93    0.0   51.2ms
    - 14   -7.251338797816       -8.76       -4.17    0.0   51.2ms

    Note that the default convergence criterion is the difference in density. When this gets below tol, the "driver" self_consistent_field artificially makes the fixed-point solver think it's converged by forcing f(x) = x. You can customize this with the is_converged keyword argument to self_consistent_field.

    + 1 -7.212054275736 -0.48 0.0 + 2 -7.245780349388 -1.47 -0.85 0.0 510ms + 3 -7.250946569942 -2.29 -1.30 0.0 95.2ms + 4 -7.251245395197 -3.52 -1.61 0.0 229ms + 5 -7.251316073962 -4.15 -1.92 0.0 90.4ms + 6 -7.251333026616 -4.77 -2.21 0.0 94.3ms + 7 -7.251337250559 -5.37 -2.50 0.0 93.4ms + 8 -7.251338358710 -5.96 -2.78 0.0 99.3ms + 9 -7.251338666610 -6.51 -3.05 0.0 95.8ms + 10 -7.251338757129 -7.04 -3.31 0.0 233ms + 11 -7.251338785114 -7.55 -3.56 0.0 90.1ms + 12 -7.251338794133 -8.04 -3.81 0.0 94.2ms + 13 -7.251338797134 -8.52 -4.05 0.0 96.2ms

    Note that the default convergence criterion is the difference in density. When this gets below tol, the "driver" self_consistent_field artificially makes the fixed-point solver think it's converged by forcing f(x) = x. You can customize this with the is_converged keyword argument to self_consistent_field.

    diff --git a/dev/examples/dielectric.ipynb b/dev/examples/dielectric.ipynb index db87a2c38c..7468f8a51c 100644 --- a/dev/examples/dielectric.ipynb +++ b/dev/examples/dielectric.ipynb @@ -17,24 +17,24 @@ "text": [ "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -7.235104628024 -0.50 9.0 \n", - " 2 -7.250458031463 -1.81 -1.40 1.0 5.13ms\n", - " 3 -7.251195468465 -3.13 -2.18 1.0 5.06ms\n", - " 4 -7.251192058559 + -5.47 -2.07 2.0 6.26ms\n", - " 5 -7.251336657333 -3.84 -2.76 1.0 5.15ms\n", - " 6 -7.251338549983 -5.72 -3.42 1.0 5.15ms\n", - " 7 -7.251338712119 -6.79 -3.56 2.0 6.34ms\n", - " 8 -7.251338796153 -7.08 -4.26 1.0 5.37ms\n", - " 9 -7.251338798349 -8.66 -5.03 1.0 5.45ms\n", - " 10 -7.251338798692 -9.46 -5.29 2.0 6.68ms\n", - " 11 -7.251338798703 -10.97 -5.80 1.0 5.63ms\n", - " 12 -7.251338798704 -11.90 -6.02 2.0 6.75ms\n", - " 13 -7.251338798704 -12.42 -6.50 1.0 5.75ms\n", - " 14 -7.251338798705 -13.18 -6.98 3.0 7.26ms\n", - " 15 -7.251338798705 -13.91 -7.45 1.0 42.5ms\n", - " 16 -7.251338798705 + -15.05 -7.88 3.0 7.97ms\n", - " 17 -7.251338798705 -14.35 -7.95 2.0 6.74ms\n", - " 18 -7.251338798705 + -14.75 -8.30 1.0 5.70ms\n" + " 1 -7.233911435116 -0.50 8.0 \n", + " 2 -7.249835417358 -1.80 -1.38 1.0 12.0ms\n", + " 3 -7.251061175072 -2.91 -1.87 2.0 14.3ms\n", + " 4 -7.251035623181 + -4.59 -1.91 1.0 11.6ms\n", + " 5 -7.251324536804 -3.54 -2.59 1.0 11.5ms\n", + " 6 -7.251337607301 -4.88 -3.15 1.0 14.1ms\n", + " 7 -7.251338653455 -5.98 -3.52 2.0 17.8ms\n", + " 8 -7.251338782571 -6.89 -4.06 1.0 13.8ms\n", + " 9 -7.251338796818 -7.85 -4.35 3.0 18.9ms\n", + " 10 -7.251338798348 -8.82 -4.80 1.0 14.2ms\n", + " 11 -7.251338798668 -9.49 -5.44 3.0 17.7ms\n", + " 12 -7.251338798697 -10.53 -5.60 2.0 16.0ms\n", + " 13 -7.251338798703 -11.26 -5.98 1.0 13.2ms\n", + " 14 -7.251338798704 -11.91 -6.33 2.0 16.0ms\n", + " 15 -7.251338798705 -12.56 -6.93 2.0 15.4ms\n", + " 16 -7.251338798705 -13.75 -7.54 2.0 16.3ms\n", + " 17 -7.251338798705 + -Inf -7.88 2.0 15.7ms\n", + " 18 -7.251338798705 -14.75 -8.47 1.0 13.3ms\n" ] } ], @@ -97,72 +97,70 @@ "name": "stdout", "output_type": "stream", "text": [ - "[ Info: Arnoldi iteration step 1: normres = 0.055312219919247715\n", - "[ Info: Arnoldi iteration step 2: normres = 0.6220609624568683\n", - "[ Info: Arnoldi iteration step 3: normres = 0.6477624268987427\n", - "[ Info: Arnoldi iteration step 4: normres = 0.3163735132009867\n", - "[ Info: Arnoldi iteration step 5: normres = 0.3162829214345903\n", - "[ Info: Arnoldi schursolve in iter 1, krylovdim = 5: 0 values converged, normres = (1.05e-02, 6.86e-02, 2.47e-01, 1.82e-01, 3.01e-02)\n", - "[ Info: Arnoldi iteration step 6: normres = 0.4660566909140053\n", - "[ Info: Arnoldi schursolve in iter 1, krylovdim = 6: 0 values converged, normres = (3.89e-03, 1.05e-01, 4.29e-01, 1.28e-01, 7.43e-02)\n", - "[ Info: Arnoldi iteration step 7: normres = 0.09439006106637726\n", - "[ Info: Arnoldi schursolve in iter 1, krylovdim = 7: 0 values converged, normres = (1.96e-04, 1.74e-02, 3.35e-02, 4.22e-02, 6.48e-02)\n", - "[ Info: Arnoldi iteration step 8: normres = 0.11031257454416346\n", - "[ Info: Arnoldi schursolve in iter 1, krylovdim = 8: 0 values converged, normres = (9.51e-06, 1.41e-03, 3.01e-03, 1.73e-02, 6.14e-02)\n", - "[ Info: Arnoldi iteration step 9: normres = 0.08158305041764619\n", - "[ Info: Arnoldi schursolve in iter 1, krylovdim = 9: 0 values converged, normres = (3.38e-07, 8.31e-05, 1.97e-04, 5.17e-03, 3.78e-02)\n", - "[ Info: Arnoldi iteration step 10: normres = 0.09782840651695524\n", - "[ Info: Arnoldi schursolve in iter 1, krylovdim = 10: 0 values converged, normres = (1.43e-08, 5.79e-06, 1.52e-05, 1.72e-03, 2.65e-02)\n", - "[ Info: Arnoldi iteration step 11: normres = 0.06048879305850886\n", - "[ Info: Arnoldi schursolve in iter 1, krylovdim = 11: 0 values converged, normres = (3.75e-10, 2.51e-07, 7.33e-07, 3.62e-04, 1.30e-02)\n", - "[ Info: Arnoldi iteration step 12: normres = 0.09401846672110832\n", - "[ Info: Arnoldi schursolve in iter 1, krylovdim = 12: 0 values converged, normres = (1.51e-11, 1.66e-08, 5.38e-08, 1.05e-04, 7.54e-03)\n", - "[ Info: Arnoldi iteration step 13: normres = 0.026695283369481\n", - "[ Info: Arnoldi schursolve in iter 1, krylovdim = 13: 1 values converged, normres = (1.72e-13, 3.08e-10, 1.11e-09, 8.66e-06, 1.33e-03)\n", - "[ Info: Arnoldi iteration step 14: normres = 0.37643326966668283\n", - "[ Info: Arnoldi schursolve in iter 1, krylovdim = 14: 1 values converged, normres = (2.77e-14, 8.08e-11, 3.21e-10, 9.36e-06, 2.49e-03)\n", - "[ Info: Arnoldi iteration step 15: normres = 0.09738286324648876\n", - "[ Info: Arnoldi schursolve in iter 1, krylovdim = 15: 1 values converged, normres = (2.46e-15, 5.05e-11, 4.11e-09, 9.44e-02, 5.44e-05)\n", - "[ Info: Arnoldi iteration step 16: normres = 0.2320255120799087\n", - "[ Info: Arnoldi schursolve in iter 1, krylovdim = 16: 1 values converged, normres = (2.43e-16, 8.54e-12, 1.65e-02, 1.56e-03, 4.15e-07)\n", - "[ Info: Arnoldi iteration step 17: normres = 0.1707876203362799\n", - "[ Info: Arnoldi schursolve in iter 1, krylovdim = 17: 1 values converged, normres = (4.41e-17, 7.63e-11, 1.66e-01, 2.65e-03, 2.52e-02)\n", - "[ Info: Arnoldi iteration step 18: normres = 0.01933083909101613\n", - "[ Info: Arnoldi schursolve in iter 1, krylovdim = 18: 1 values converged, normres = (3.58e-19, 1.89e-03, 9.93e-04, 2.76e-04, 1.72e-05)\n", - "[ Info: Arnoldi iteration step 19: normres = 0.11749014564267883\n", - "[ Info: Arnoldi schursolve in iter 1, krylovdim = 19: 1 values converged, normres = (1.76e-20, 7.00e-10, 1.69e-04, 1.41e-08, 2.42e-05)\n", - "[ Info: Arnoldi iteration step 20: normres = 0.11477489173133563\n", - "[ Info: Arnoldi schursolve in iter 1, krylovdim = 20: 1 values converged, normres = (9.85e-22, 1.06e-05, 1.32e-05, 2.53e-06, 1.16e-06)\n", - "[ Info: Arnoldi iteration step 21: normres = 0.025697213786413778\n", - "[ Info: Arnoldi schursolve in iter 1, krylovdim = 21: 1 values converged, normres = (1.07e-23, 2.97e-07, 3.36e-08, 2.59e-08, 4.76e-08)\n", - "[ Info: Arnoldi iteration step 22: normres = 0.014598307041582723\n", - "[ Info: Arnoldi schursolve in iter 1, krylovdim = 22: 1 values converged, normres = (6.38e-26, 2.42e-11, 2.84e-09, 5.18e-10, 2.32e-10)\n", - "[ Info: Arnoldi iteration step 23: normres = 0.3244893813585307\n", - "[ Info: Arnoldi schursolve in iter 1, krylovdim = 23: 1 values converged, normres = (8.80e-27, 6.07e-12, 6.38e-10, 1.33e-10, 4.72e-11)\n", - "[ Info: Arnoldi iteration step 24: normres = 0.05732329914163686\n", - "[ Info: Arnoldi schursolve in iter 1, krylovdim = 24: 1 values converged, normres = (4.64e-28, 2.33e-12, 2.44e-10, 3.99e-09, 4.64e-09)\n", - "[ Info: Arnoldi iteration step 25: normres = 0.03649511097711554\n", - "[ Info: Arnoldi schursolve in iter 1, krylovdim = 25: 2 values converged, normres = (6.95e-30, 5.56e-14, 5.87e-12, 3.45e-09, 3.25e-11)\n", - "[ Info: Arnoldi iteration step 26: normres = 0.09427486177240832\n", - "[ Info: Arnoldi schursolve in iter 1, krylovdim = 26: 3 values converged, normres = (2.93e-31, 3.94e-15, 4.16e-13, 7.63e-05, 1.19e-05)\n", - "[ Info: Arnoldi iteration step 27: normres = 0.024979824970092938\n", - "[ Info: Arnoldi schursolve in iter 1, krylovdim = 27: 3 values converged, normres = (3.05e-33, 6.61e-17, 6.97e-15, 1.04e-06, 7.52e-07)\n", - "[ Info: Arnoldi iteration step 28: normres = 0.10593443225743111\n", - "[ Info: Arnoldi schursolve in iter 1, krylovdim = 28: 3 values converged, normres = (1.40e-34, 5.01e-18, 5.29e-16, 9.34e-08, 3.83e-08)\n", - "[ Info: Arnoldi iteration step 29: normres = 0.029251334899260385\n", - "[ Info: Arnoldi schursolve in iter 1, krylovdim = 29: 3 values converged, normres = (1.73e-36, 1.00e-19, 1.06e-17, 1.79e-09, 1.88e-09)\n", - "[ Info: Arnoldi iteration step 30: normres = 0.21718526992108503\n", - "[ Info: Arnoldi schursolve in iter 1, krylovdim = 30: 3 values converged, normres = (1.74e-37, 1.75e-20, 1.84e-18, 3.00e-10, 5.30e-10)\n", - "[ Info: Arnoldi schursolve in iter 2, krylovdim = 19: 3 values converged, normres = (1.74e-37, 1.75e-20, 1.84e-18, 3.00e-10, 5.30e-10)\n", - "[ Info: Arnoldi iteration step 20: normres = 0.0790515987299459\n", - "[ Info: Arnoldi schursolve in iter 2, krylovdim = 20: 3 values converged, normres = (6.17e-39, 1.05e-21, 1.11e-19, 2.10e-11, 3.25e-11)\n", - "[ Info: Arnoldi iteration step 21: normres = 0.04119484576943037\n", - "[ Info: Arnoldi schursolve in iter 2, krylovdim = 21: 4 values converged, normres = (1.09e-40, 3.05e-23, 3.22e-21, 6.80e-13, 1.05e-12)\n", - "[ Info: Arnoldi iteration step 22: normres = 0.01818899401646549\n", + "[ Info: Arnoldi iteration step 1: normres = 0.054052209588599145\n", + "[ Info: Arnoldi iteration step 2: normres = 0.33964339636786217\n", + "[ Info: Arnoldi iteration step 3: normres = 0.3507620012651574\n", + "[ Info: Arnoldi iteration step 4: normres = 0.961673847113989\n", + "[ Info: Arnoldi iteration step 5: normres = 0.30980174396337945\n", + "[ Info: Arnoldi schursolve in iter 1, krylovdim = 5: 0 values converged, normres = (1.57e-01, 2.85e-02, 1.93e-01, 1.80e-01, 2.62e-02)\n", + "[ Info: Arnoldi iteration step 6: normres = 0.5982077185216905\n", + "[ Info: Arnoldi schursolve in iter 1, krylovdim = 6: 0 values converged, normres = (7.20e-02, 6.44e-02, 5.64e-01, 1.29e-01, 1.03e-01)\n", + "[ Info: Arnoldi iteration step 7: normres = 0.06851835883642277\n", + "[ Info: Arnoldi schursolve in iter 1, krylovdim = 7: 0 values converged, normres = (3.17e-03, 3.59e-02, 6.95e-03, 3.06e-02, 4.15e-02)\n", + "[ Info: Arnoldi iteration step 8: normres = 0.1184371868886506\n", + "[ Info: Arnoldi schursolve in iter 1, krylovdim = 8: 0 values converged, normres = (1.62e-04, 3.01e-03, 6.31e-04, 1.12e-02, 3.80e-02)\n", + "[ Info: Arnoldi iteration step 9: normres = 0.06801754859834616\n", + "[ Info: Arnoldi schursolve in iter 1, krylovdim = 9: 0 values converged, normres = (4.76e-06, 1.46e-04, 3.41e-05, 2.67e-03, 3.22e-02)\n", + "[ Info: Arnoldi iteration step 10: normres = 0.11455675468747246\n", + "[ Info: Arnoldi schursolve in iter 1, krylovdim = 10: 0 values converged, normres = (2.40e-07, 1.22e-05, 3.19e-06, 1.16e-03, 4.22e-02)\n", + "[ Info: Arnoldi iteration step 11: normres = 0.07587538547516845\n", + "[ Info: Arnoldi schursolve in iter 1, krylovdim = 11: 0 values converged, normres = (7.90e-09, 6.67e-07, 1.93e-07, 3.26e-04, 3.32e-02)\n", + "[ Info: Arnoldi iteration step 12: normres = 0.07227614448449163\n", + "[ Info: Arnoldi schursolve in iter 1, krylovdim = 12: 0 values converged, normres = (2.43e-10, 3.35e-08, 1.08e-08, 7.08e-05, 1.31e-02)\n", + "[ Info: Arnoldi iteration step 13: normres = 0.04457500803376656\n", + "[ Info: Arnoldi schursolve in iter 1, krylovdim = 13: 0 values converged, normres = (4.63e-12, 1.04e-09, 3.70e-10, 9.35e-06, 3.23e-03)\n", + "[ Info: Arnoldi iteration step 14: normres = 0.6954703006303573\n", + "[ Info: Arnoldi schursolve in iter 1, krylovdim = 14: 0 values converged, normres = (1.86e-12, 8.76e-10, 3.74e-10, 6.76e-01, 8.04e-02)\n", + "[ Info: Arnoldi iteration step 15: normres = 0.04607745707525615\n", + "[ Info: Arnoldi schursolve in iter 1, krylovdim = 15: 1 values converged, normres = (5.85e-14, 1.60e-10, 3.20e-02, 4.31e-03, 1.76e-05)\n", + "[ Info: Arnoldi iteration step 16: normres = 0.7392525326056035\n", + "[ Info: Arnoldi schursolve in iter 1, krylovdim = 16: 1 values converged, normres = (2.47e-14, 1.42e-10, 3.35e-02, 3.78e-03, 7.30e-01)\n", + "[ Info: Arnoldi iteration step 17: normres = 0.042124533611050334\n", + "[ Info: Arnoldi schursolve in iter 1, krylovdim = 17: 1 values converged, normres = (8.32e-16, 5.13e-03, 3.05e-02, 7.90e-04, 5.27e-03)\n", + "[ Info: Arnoldi iteration step 18: normres = 0.017997558886386534\n", + "[ Info: Arnoldi schursolve in iter 1, krylovdim = 18: 1 values converged, normres = (6.17e-18, 2.56e-09, 3.66e-04, 1.36e-08, 6.98e-05)\n", + "[ Info: Arnoldi iteration step 19: normres = 0.20942796382087553\n", + "[ Info: Arnoldi schursolve in iter 1, krylovdim = 19: 1 values converged, normres = (5.53e-19, 5.17e-05, 1.50e-05, 1.12e-05, 1.62e-06)\n", + "[ Info: Arnoldi iteration step 20: normres = 0.03191172580063678\n", + "[ Info: Arnoldi schursolve in iter 1, krylovdim = 20: 1 values converged, normres = (8.44e-21, 9.04e-07, 1.15e-06, 3.50e-07, 2.36e-08)\n", + "[ Info: Arnoldi iteration step 21: normres = 0.026504704752375086\n", + "[ Info: Arnoldi schursolve in iter 1, krylovdim = 21: 1 values converged, normres = (9.19e-23, 1.28e-09, 2.53e-08, 3.32e-09, 5.81e-09)\n", + "[ Info: Arnoldi iteration step 22: normres = 0.09660798354671142\n", + "[ Info: Arnoldi schursolve in iter 1, krylovdim = 22: 1 values converged, normres = (3.71e-24, 1.98e-10, 1.64e-09, 2.92e-10, 3.83e-10)\n", + "[ Info: Arnoldi iteration step 23: normres = 0.3934755324913308\n", + "[ Info: Arnoldi schursolve in iter 1, krylovdim = 23: 1 values converged, normres = (1.23e-24, 2.47e-10, 2.32e-09, 8.49e-10, 1.08e-09)\n", + "[ Info: Arnoldi iteration step 24: normres = 0.01690571822085645\n", + "[ Info: Arnoldi schursolve in iter 1, krylovdim = 24: 1 values converged, normres = (9.68e-27, 5.98e-12, 5.11e-11, 3.33e-03, 3.50e-03)\n", + "[ Info: Arnoldi iteration step 25: normres = 0.03659335292421494\n", + "[ Info: Arnoldi schursolve in iter 1, krylovdim = 25: 2 values converged, normres = (1.45e-28, 1.43e-13, 1.22e-12, 3.37e-09, 6.94e-09)\n", + "[ Info: Arnoldi iteration step 26: normres = 0.08793706955269852\n", + "[ Info: Arnoldi schursolve in iter 1, krylovdim = 26: 3 values converged, normres = (5.66e-30, 9.28e-15, 7.92e-14, 2.32e-06, 2.01e-06)\n", + "[ Info: Arnoldi iteration step 27: normres = 0.05484231483777693\n", + "[ Info: Arnoldi schursolve in iter 1, krylovdim = 27: 3 values converged, normres = (1.30e-31, 3.46e-16, 2.95e-15, 2.20e-07, 2.79e-07)\n", + "[ Info: Arnoldi iteration step 28: normres = 0.057547014562007226\n", + "[ Info: Arnoldi schursolve in iter 1, krylovdim = 28: 3 values converged, normres = (3.34e-33, 1.49e-17, 1.27e-16, 1.84e-08, 1.78e-09)\n", + "[ Info: Arnoldi iteration step 29: normres = 0.0635431308878074\n", + "[ Info: Arnoldi schursolve in iter 1, krylovdim = 29: 3 values converged, normres = (8.79e-35, 6.27e-19, 5.35e-18, 8.74e-10, 2.08e-11)\n", + "[ Info: Arnoldi iteration step 30: normres = 0.08287179407487535\n", + "[ Info: Arnoldi schursolve in iter 1, krylovdim = 30: 3 values converged, normres = (3.60e-36, 4.65e-20, 3.96e-19, 7.40e-11, 2.10e-12)\n", + "[ Info: Arnoldi schursolve in iter 2, krylovdim = 19: 3 values converged, normres = (3.60e-36, 4.65e-20, 3.96e-19, 7.40e-11, 2.10e-12)\n", + "[ Info: Arnoldi iteration step 20: normres = 0.04518409812494398\n", + "[ Info: Arnoldi schursolve in iter 2, krylovdim = 20: 3 values converged, normres = (6.77e-38, 1.41e-21, 1.20e-20, 2.47e-12, 6.65e-14)\n", + "[ Info: Arnoldi iteration step 21: normres = 0.052552462269561555\n", "┌ Info: Arnoldi eigsolve finished after 2 iterations:\n", "│ * 6 eigenvalues converged\n", - "│ * norm of residuals = (8.181919428345824e-43, 3.642359317209742e-25, 3.843314134269544e-23, 8.922341263893555e-15, 1.6365905384309256e-14, 1.6459314140496273e-14)\n", - "└ * number of operations = 33\n" + "│ * norm of residuals = (1.5327946026801528e-39, 5.2308055800927114e-23, 4.48005303260869e-22, 1.0197657695842201e-13, 3.1364767640923344e-14, 3.0769636362672125e-14)\n", + "└ * number of operations = 32\n" ] } ], diff --git a/dev/examples/dielectric/index.html b/dev/examples/dielectric/index.html index b977b4d927..cfe1970b53 100644 --- a/dev/examples/dielectric/index.html +++ b/dev/examples/dielectric/index.html @@ -20,90 +20,88 @@ basis = PlaneWaveBasis(model; Ecut, kgrid) scfres = self_consistent_field(basis, tol=1e-8);
    n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime
     ---   ---------------   ---------   ---------   ----   ------
    -  1   -7.234161707615                   -0.50    8.0
    -  2   -7.249775211609       -1.81       -1.37    1.0   4.94ms
    -  3   -7.249958348688       -3.74       -1.59    2.0   5.92ms
    -  4   -7.250984431358       -2.99       -1.88    1.0   5.09ms
    -  5   -7.251326272497       -3.47       -2.64    1.0   5.13ms
    -  6   -7.251337332339       -4.96       -3.02    2.0   6.24ms
    -  7   -7.251338709511       -5.86       -3.76    2.0   6.31ms
    -  8   -7.251338791282       -7.09       -4.17    3.0   7.58ms
    -  9   -7.251338797985       -8.17       -4.64    2.0   6.31ms
    - 10   -7.251338798647       -9.18       -5.25    1.0   5.52ms
    - 11   -7.251338798700      -10.27       -5.67    3.0   7.61ms
    - 12   -7.251338798704      -11.42       -6.21    1.0   5.71ms
    - 13   -7.251338798704      -12.23       -6.65    3.0   7.45ms
    - 14   -7.251338798705      -13.35       -6.92    2.0   6.72ms
    - 15   -7.251338798705      -13.97       -7.20    1.0   5.70ms
    - 16   -7.251338798705      -14.75       -7.82    1.0   5.73ms
    - 17   -7.251338798705      -14.75       -7.92    3.0   7.57ms
    - 18   -7.251338798705   +  -15.05       -8.28    1.0   5.72ms

    Applying $ε^† ≔ (1- χ_0 K)$

    function eps_fun(δρ)
    +  1   -7.233930824399                   -0.50    8.0
    +  2   -7.249964876859       -1.79       -1.40    1.0   15.6ms
    +  3   -7.251004702953       -2.98       -1.88    2.0   12.6ms
    +  4   -7.250910315527   +   -4.03       -1.85    2.0   14.1ms
    +  5   -7.251332496686       -3.37       -2.73    1.0   11.1ms
    +  6   -7.251337958755       -5.26       -3.15    1.0   12.2ms
    +  7   -7.251338658485       -6.16       -3.49    2.0   13.8ms
    +  8   -7.251338780206       -6.91       -3.96    1.0   10.9ms
    +  9   -7.251338796820       -7.78       -4.39    2.0   13.9ms
    + 10   -7.251338798313       -8.83       -4.75    1.0   11.3ms
    + 11   -7.251338798639       -9.49       -5.21    2.0   13.7ms
    + 12   -7.251338798700      -10.21       -5.76    2.0   13.9ms
    + 13   -7.251338798704      -11.38       -6.23    2.0   14.0ms
    + 14   -7.251338798704      -12.52       -6.54    2.0   13.5ms
    + 15   -7.251338798705      -13.32       -6.94    1.0   12.4ms
    + 16   -7.251338798705      -13.75       -7.42    2.0    109ms
    + 17   -7.251338798705      -14.75       -7.85    2.0   13.7ms
    + 18   -7.251338798705      -14.57       -8.32    2.0   14.2ms

    Applying $ε^† ≔ (1- χ_0 K)$

    function eps_fun(δρ)
         δV = apply_kernel(basis, δρ; ρ=scfres.ρ)
         χ0δV = apply_χ0(scfres, δV)
         δρ - χ0δV
    -end;

    … eagerly diagonalizes the subspace matrix at each iteration

    eigsolve(eps_fun, randn(size(scfres.ρ)), 5, :LM; eager=true, verbosity=3);
    [ Info: Arnoldi iteration step 1: normres = 0.05267563367708389
    -[ Info: Arnoldi iteration step 2: normres = 0.43605964653603235
    -[ Info: Arnoldi iteration step 3: normres = 0.4044882500189541
    -[ Info: Arnoldi iteration step 4: normres = 0.890502758384252
    -[ Info: Arnoldi iteration step 5: normres = 0.5042428326059628
    -[ Info: Arnoldi schursolve in iter 1, krylovdim = 5: 0 values converged, normres = (2.53e-01, 7.52e-02, 3.38e-01, 2.66e-01, 1.71e-02)
    -[ Info: Arnoldi iteration step 6: normres = 0.3722573618223099
    -[ Info: Arnoldi schursolve in iter 1, krylovdim = 6: 0 values converged, normres = (8.61e-02, 2.05e-01, 2.60e-01, 1.04e-01, 9.64e-02)
    -[ Info: Arnoldi iteration step 7: normres = 0.09595620692289505
    -[ Info: Arnoldi schursolve in iter 1, krylovdim = 7: 0 values converged, normres = (4.10e-03, 2.07e-02, 1.65e-02, 4.33e-02, 7.10e-02)
    -[ Info: Arnoldi iteration step 8: normres = 0.09196815197836773
    -[ Info: Arnoldi schursolve in iter 1, krylovdim = 8: 0 values converged, normres = (1.66e-04, 1.40e-03, 1.22e-03, 1.50e-02, 4.70e-02)
    -[ Info: Arnoldi iteration step 9: normres = 0.09276348629014154
    -[ Info: Arnoldi schursolve in iter 1, krylovdim = 9: 0 values converged, normres = (6.64e-06, 9.26e-05, 8.96e-05, 4.80e-03, 3.03e-02)
    -[ Info: Arnoldi iteration step 10: normres = 0.08845543910711803
    -[ Info: Arnoldi schursolve in iter 1, krylovdim = 10: 0 values converged, normres = (2.56e-07, 5.90e-06, 6.35e-06, 1.51e-03, 2.21e-02)
    -[ Info: Arnoldi iteration step 11: normres = 0.053079666706014085
    -[ Info: Arnoldi schursolve in iter 1, krylovdim = 11: 0 values converged, normres = (5.83e-09, 2.20e-07, 2.63e-07, 2.58e-04, 8.50e-03)
    -[ Info: Arnoldi iteration step 12: normres = 0.09372466159314174
    -[ Info: Arnoldi schursolve in iter 1, krylovdim = 12: 0 values converged, normres = (2.33e-10, 1.44e-08, 1.90e-08, 7.04e-05, 4.15e-03)
    -[ Info: Arnoldi iteration step 13: normres = 0.03092331957310417
    -[ Info: Arnoldi schursolve in iter 1, krylovdim = 13: 0 values converged, normres = (3.08e-12, 3.11e-10, 4.56e-10, 6.81e-06, 8.53e-04)
    -[ Info: Arnoldi iteration step 14: normres = 0.3781397549412105
    -[ Info: Arnoldi schursolve in iter 1, krylovdim = 14: 1 values converged, normres = (5.02e-13, 8.32e-11, 1.35e-10, 7.96e-06, 1.85e-03)
    -[ Info: Arnoldi iteration step 15: normres = 0.11235600232303006
    -[ Info: Arnoldi schursolve in iter 1, krylovdim = 15: 1 values converged, normres = (5.30e-14, 7.84e-11, 1.07e-01, 8.64e-03, 5.47e-05)
    -[ Info: Arnoldi iteration step 16: normres = 0.6894392575919692
    -[ Info: Arnoldi schursolve in iter 1, krylovdim = 16: 1 values converged, normres = (1.87e-14, 5.61e-11, 7.66e-02, 6.75e-04, 6.32e-01)
    -[ Info: Arnoldi iteration step 17: normres = 0.042264627955742456
    -[ Info: Arnoldi schursolve in iter 1, krylovdim = 17: 1 values converged, normres = (6.87e-16, 5.01e-09, 3.00e-02, 7.92e-05, 1.94e-02)
    -[ Info: Arnoldi iteration step 18: normres = 0.014784444874451698
    -[ Info: Arnoldi schursolve in iter 1, krylovdim = 18: 1 values converged, normres = (4.18e-18, 9.55e-10, 2.93e-04, 1.99e-04, 3.56e-05)
    -[ Info: Arnoldi iteration step 19: normres = 0.2081846980688425
    -[ Info: Arnoldi schursolve in iter 1, krylovdim = 19: 1 values converged, normres = (3.76e-19, 4.33e-05, 2.90e-06, 3.31e-09, 3.33e-05)
    -[ Info: Arnoldi iteration step 20: normres = 0.04607764872987395
    -[ Info: Arnoldi schursolve in iter 1, krylovdim = 20: 1 values converged, normres = (8.25e-21, 1.89e-09, 1.69e-06, 1.06e-06, 1.02e-06)
    -[ Info: Arnoldi iteration step 21: normres = 0.03458367987751148
    -[ Info: Arnoldi schursolve in iter 1, krylovdim = 21: 1 values converged, normres = (1.19e-22, 9.55e-09, 3.78e-08, 7.77e-09, 3.67e-08)
    -[ Info: Arnoldi iteration step 22: normres = 0.014999411342839367
    -[ Info: Arnoldi schursolve in iter 1, krylovdim = 22: 1 values converged, normres = (7.36e-25, 2.92e-10, 2.54e-10, 5.17e-11, 4.07e-10)
    -[ Info: Arnoldi iteration step 23: normres = 0.4712672202762636
    -[ Info: Arnoldi schursolve in iter 1, krylovdim = 23: 1 values converged, normres = (1.51e-25, 9.66e-11, 8.87e-11, 1.96e-11, 1.53e-10)
    -[ Info: Arnoldi iteration step 24: normres = 0.054049399065490465
    -[ Info: Arnoldi schursolve in iter 1, krylovdim = 24: 1 values converged, normres = (7.32e-27, 3.34e-11, 3.04e-11, 4.04e-10, 2.95e-09)
    -[ Info: Arnoldi iteration step 25: normres = 0.016867448667214513
    -[ Info: Arnoldi schursolve in iter 1, krylovdim = 25: 3 values converged, normres = (5.07e-29, 3.73e-13, 3.41e-13, 3.12e-10, 2.79e-09)
    -[ Info: Arnoldi iteration step 26: normres = 0.1240695493332096
    -[ Info: Arnoldi schursolve in iter 1, krylovdim = 26: 3 values converged, normres = (2.73e-30, 3.31e-14, 3.03e-14, 5.63e-05, 1.17e-05)
    -[ Info: Arnoldi iteration step 27: normres = 0.04067433996817177
    -[ Info: Arnoldi schursolve in iter 1, krylovdim = 27: 3 values converged, normres = (4.76e-32, 9.47e-16, 8.66e-16, 5.04e-07, 1.74e-07)
    -[ Info: Arnoldi iteration step 28: normres = 0.10600548320357069
    -[ Info: Arnoldi schursolve in iter 1, krylovdim = 28: 3 values converged, normres = (2.16e-33, 7.01e-17, 6.41e-17, 6.36e-08, 1.19e-07)
    -[ Info: Arnoldi iteration step 29: normres = 0.03253170236006339
    -[ Info: Arnoldi schursolve in iter 1, krylovdim = 29: 3 values converged, normres = (3.00e-35, 1.60e-18, 1.46e-18, 3.54e-10, 3.99e-09)
    -[ Info: Arnoldi iteration step 30: normres = 0.12375634243414928
    -[ Info: Arnoldi schursolve in iter 1, krylovdim = 30: 3 values converged, normres = (1.63e-36, 1.44e-19, 1.32e-19, 2.42e-11, 3.65e-10)
    -[ Info: Arnoldi schursolve in iter 2, krylovdim = 19: 3 values converged, normres = (1.63e-36, 1.44e-19, 1.32e-19, 2.42e-11, 3.65e-10)
    -[ Info: Arnoldi iteration step 20: normres = 0.1848703749305204
    -[ Info: Arnoldi schursolve in iter 2, krylovdim = 20: 3 values converged, normres = (1.41e-37, 2.18e-20, 1.99e-20, 4.11e-12, 6.23e-11)
    -[ Info: Arnoldi iteration step 21: normres = 0.03412590841946766
    -[ Info: Arnoldi schursolve in iter 2, krylovdim = 21: 4 values converged, normres = (2.11e-39, 5.42e-22, 4.96e-22, 1.15e-13, 1.73e-12)
    -[ Info: Arnoldi iteration step 22: normres = 0.01465314285328348
    +end;

    … eagerly diagonalizes the subspace matrix at each iteration

    eigsolve(eps_fun, randn(size(scfres.ρ)), 5, :LM; eager=true, verbosity=3);
    [ Info: Arnoldi iteration step 1: normres = 0.06542267650923701
    +[ Info: Arnoldi iteration step 2: normres = 0.5636384804107808
    +[ Info: Arnoldi iteration step 3: normres = 0.9381364728307773
    +[ Info: Arnoldi iteration step 4: normres = 0.2432854260907509
    +[ Info: Arnoldi iteration step 5: normres = 0.5218537201847657
    +[ Info: Arnoldi schursolve in iter 1, krylovdim = 5: 0 values converged, normres = (3.52e-02, 7.17e-02, 4.83e-01, 1.81e-01, 4.11e-03)
    +[ Info: Arnoldi iteration step 6: normres = 0.31224648782296954
    +[ Info: Arnoldi schursolve in iter 1, krylovdim = 6: 0 values converged, normres = (8.65e-03, 9.76e-02, 2.36e-01, 1.24e-01, 1.25e-01)
    +[ Info: Arnoldi iteration step 7: normres = 0.07076651668167937
    +[ Info: Arnoldi schursolve in iter 1, krylovdim = 7: 0 values converged, normres = (2.98e-04, 7.49e-03, 1.43e-02, 4.36e-02, 4.08e-02)
    +[ Info: Arnoldi iteration step 8: normres = 0.10985201526059493
    +[ Info: Arnoldi schursolve in iter 1, krylovdim = 8: 0 values converged, normres = (1.41e-05, 5.87e-04, 1.24e-03, 1.52e-02, 3.80e-02)
    +[ Info: Arnoldi iteration step 9: normres = 0.05408191727154577
    +[ Info: Arnoldi schursolve in iter 1, krylovdim = 9: 0 values converged, normres = (3.26e-07, 2.21e-05, 5.17e-05, 2.52e-03, 1.89e-02)
    +[ Info: Arnoldi iteration step 10: normres = 0.0855673313086333
    +[ Info: Arnoldi schursolve in iter 1, krylovdim = 10: 0 values converged, normres = (1.20e-08, 1.34e-06, 3.48e-06, 6.88e-04, 1.55e-02)
    +[ Info: Arnoldi iteration step 11: normres = 0.08783453980762324
    +[ Info: Arnoldi schursolve in iter 1, krylovdim = 11: 0 values converged, normres = (4.54e-10, 8.28e-08, 2.39e-07, 1.89e-04, 1.39e-02)
    +[ Info: Arnoldi iteration step 12: normres = 0.1058018185398012
    +[ Info: Arnoldi schursolve in iter 1, krylovdim = 12: 0 values converged, normres = (2.14e-11, 6.57e-09, 2.12e-08, 8.81e-05, 6.43e-02)
    +[ Info: Arnoldi iteration step 13: normres = 0.06698155321550417
    +[ Info: Arnoldi schursolve in iter 1, krylovdim = 13: 1 values converged, normres = (6.12e-13, 3.07e-10, 1.10e-09, 1.94e-05, 2.58e-02)
    +[ Info: Arnoldi iteration step 14: normres = 0.5402677612707963
    +[ Info: Arnoldi schursolve in iter 1, krylovdim = 14: 1 values converged, normres = (2.51e-13, 4.07e-10, 2.21e-09, 5.39e-01, 1.53e-02)
    +[ Info: Arnoldi iteration step 15: normres = 0.12456528933318375
    +[ Info: Arnoldi schursolve in iter 1, krylovdim = 15: 1 values converged, normres = (1.66e-14, 1.18e-10, 5.46e-02, 1.96e-03, 3.47e-06)
    +[ Info: Arnoldi iteration step 16: normres = 0.3904475473176373
    +[ Info: Arnoldi schursolve in iter 1, krylovdim = 16: 1 values converged, normres = (6.24e-15, 4.22e-10, 2.52e-01, 3.06e-02, 2.94e-01)
    +[ Info: Arnoldi iteration step 17: normres = 0.024664263848353966
    +[ Info: Arnoldi schursolve in iter 1, krylovdim = 17: 1 values converged, normres = (7.16e-17, 3.22e-09, 6.06e-03, 2.83e-03, 1.09e-03)
    +[ Info: Arnoldi iteration step 18: normres = 0.017012403687592423
    +[ Info: Arnoldi schursolve in iter 1, krylovdim = 18: 1 values converged, normres = (5.01e-19, 1.75e-05, 6.52e-05, 3.67e-05, 5.74e-06)
    +[ Info: Arnoldi iteration step 19: normres = 0.15237489312068317
    +[ Info: Arnoldi schursolve in iter 1, krylovdim = 19: 1 values converged, normres = (3.18e-20, 2.22e-08, 6.89e-06, 9.18e-08, 4.19e-06)
    +[ Info: Arnoldi iteration step 20: normres = 0.04626385688944236
    +[ Info: Arnoldi schursolve in iter 1, krylovdim = 20: 1 values converged, normres = (7.22e-22, 3.97e-09, 2.83e-07, 6.60e-08, 1.85e-07)
    +[ Info: Arnoldi iteration step 21: normres = 0.03616538122759118
    +[ Info: Arnoldi schursolve in iter 1, krylovdim = 21: 1 values converged, normres = (1.08e-23, 6.12e-10, 6.75e-09, 2.35e-09, 4.63e-09)
    +[ Info: Arnoldi iteration step 22: normres = 0.04703162593613228
    +[ Info: Arnoldi schursolve in iter 1, krylovdim = 22: 1 values converged, normres = (2.11e-25, 1.63e-11, 2.12e-10, 7.89e-11, 1.61e-10)
    +[ Info: Arnoldi iteration step 23: normres = 0.5923938231930491
    +[ Info: Arnoldi schursolve in iter 1, krylovdim = 23: 1 values converged, normres = (8.71e-26, 1.83e-11, 2.38e-10, 1.20e-10, 2.45e-10)
    +[ Info: Arnoldi iteration step 24: normres = 0.02040226847076983
    +[ Info: Arnoldi schursolve in iter 1, krylovdim = 24: 2 values converged, normres = (1.00e-27, 9.18e-13, 1.20e-11, 1.15e-09, 2.33e-09)
    +[ Info: Arnoldi iteration step 25: normres = 0.115121028411961
    +[ Info: Arnoldi schursolve in iter 1, krylovdim = 25: 3 values converged, normres = (4.86e-29, 7.23e-14, 9.42e-13, 5.24e-04, 7.12e-04)
    +[ Info: Arnoldi iteration step 26: normres = 0.02726247281533318
    +[ Info: Arnoldi schursolve in iter 1, krylovdim = 26: 3 values converged, normres = (5.86e-31, 1.46e-15, 1.91e-14, 5.51e-06, 3.63e-06)
    +[ Info: Arnoldi iteration step 27: normres = 0.02300246197187758
    +[ Info: Arnoldi schursolve in iter 1, krylovdim = 27: 3 values converged, normres = (5.54e-33, 2.20e-17, 2.87e-16, 1.68e-09, 4.54e-09)
    +[ Info: Arnoldi iteration step 28: normres = 0.10018514295485512
    +[ Info: Arnoldi schursolve in iter 1, krylovdim = 28: 3 values converged, normres = (2.39e-34, 1.56e-18, 2.03e-17, 1.08e-08, 2.16e-08)
    +[ Info: Arnoldi iteration step 29: normres = 0.04587005177536601
    +[ Info: Arnoldi schursolve in iter 1, krylovdim = 29: 3 values converged, normres = (4.66e-36, 4.96e-20, 6.46e-19, 5.77e-11, 9.03e-10)
    +[ Info: Arnoldi iteration step 30: normres = 0.14015242192397367
    +[ Info: Arnoldi schursolve in iter 1, krylovdim = 30: 3 values converged, normres = (3.19e-37, 6.10e-21, 7.94e-20, 7.90e-12, 1.26e-10)
    +[ Info: Arnoldi schursolve in iter 2, krylovdim = 19: 3 values converged, normres = (3.19e-37, 6.10e-21, 7.94e-20, 7.90e-12, 1.26e-10)
    +[ Info: Arnoldi iteration step 20: normres = 0.06913141747462156
    +[ Info: Arnoldi schursolve in iter 2, krylovdim = 20: 4 values converged, normres = (9.40e-39, 2.93e-22, 3.82e-21, 4.24e-13, 6.75e-12)
    +[ Info: Arnoldi iteration step 21: normres = 0.043201634188077424
     ┌ Info: Arnoldi eigsolve finished after 2 iterations:
     *  6 eigenvalues converged
    -*  norm of residuals = (1.2619784094330378e-41, 5.1557410657848885e-24, 8.900416150161804e-25, 1.1983105258307398e-15, 1.816243015291874e-14, 5.265661477403299e-15)
    -*  number of operations = 33
    +* norm of residuals = (1.7451519785701219e-40, 8.932029334538594e-24, 1.166983329497474e-22, 1.4313084437449652e-14, 2.2819764215058583e-13, 2.464335373873497e-14) +* number of operations = 32 diff --git a/dev/examples/energy_cutoff_smearing.ipynb b/dev/examples/energy_cutoff_smearing.ipynb index f8ec53171a..1816f47312 100644 --- a/dev/examples/energy_cutoff_smearing.ipynb +++ b/dev/examples/energy_cutoff_smearing.ipynb @@ -78,105 +78,105 @@ "\n", "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n" + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" ], "image/svg+xml": [ "\n", "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n" + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" ] }, "metadata": {}, @@ -270,125 +270,125 @@ "\n", "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - 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"\n", - "\n", - "\n", - "\n", - "\n", - "\n" + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" ] }, "metadata": {}, diff --git a/dev/examples/energy_cutoff_smearing/2a7c6f77.svg b/dev/examples/energy_cutoff_smearing/32843ba4.svg similarity index 86% rename from dev/examples/energy_cutoff_smearing/2a7c6f77.svg rename to dev/examples/energy_cutoff_smearing/32843ba4.svg index 3d1706d92b..8d446291c5 100644 --- a/dev/examples/energy_cutoff_smearing/2a7c6f77.svg +++ b/dev/examples/energy_cutoff_smearing/32843ba4.svg @@ -1,50 +1,50 @@ - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/examples/energy_cutoff_smearing/48491aac.svg b/dev/examples/energy_cutoff_smearing/60ba2ef3.svg similarity index 86% rename from dev/examples/energy_cutoff_smearing/48491aac.svg rename to dev/examples/energy_cutoff_smearing/60ba2ef3.svg index 735bea64d3..d990f4e891 100644 --- a/dev/examples/energy_cutoff_smearing/48491aac.svg +++ b/dev/examples/energy_cutoff_smearing/60ba2ef3.svg @@ -1,60 +1,60 @@ - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/examples/energy_cutoff_smearing/index.html b/dev/examples/energy_cutoff_smearing/index.html index abca991189..d53ff1b83b 100644 --- a/dev/examples/energy_cutoff_smearing/index.html +++ b/dev/examples/energy_cutoff_smearing/index.html @@ -31,7 +31,7 @@ shift = mean(abs.(E0_naive .- E0_ref)) p = plot(a_list, E0_naive .- shift, label="Ecut=5", xlabel="lattice parameter a (bohr)", ylabel="Ground state energy (Ha)", color=1) -plot!(p, a_list, E0_ref, label="Ecut=100", color=2)Example block output

    The problem of non-smoothness of the approximated energy is typically avoided by taking a large enough Ecut, at the cost of a high computation time. Another method consist in introducing a modified kinetic term defined through the data of a blow-up function, a method which is also referred to as "energy cutoff smearing". DFTK features energy cutoff smearing using the CHV blow-up function introduced in [CHV2022] that is mathematically ensured to provide $C^2$ regularity of the energy bands.

    Éric Cancès, Muhammad Hassan and Laurent Vidal Modified-operator method for the calculation of band diagrams of crystalline materials, 2022. arXiv preprint.

    Let us lauch the computation again with the modified kinetic term.

    E0_modified = compute_ground_state_energy.(a_list; kinetic_blowup=BlowupCHV(), Ecut, kgrid);
    Abinit energy cutoff smearing option

    For the sake of completeness, DFTK also provides the blow-up function BlowupAbinit proposed in the Abinit quantum chemistry code. This function depends on a parameter Ecutsm fixed by the user (see Abinit user guide). For the right choice of Ecutsm, BlowupAbinit corresponds to the BlowupCHV approach with coefficients ensuring $C^1$ regularity. To choose BlowupAbinit, pass kinetic_blowup=BlowupAbinit(Ecutsm) to the model constructors.

    We can know compare the approximation of the energy as well as the estimated lattice constant for each strategy.

    estimate_a0(E0_values) = a_list[findmin(E0_values)[2]]
    +plot!(p, a_list, E0_ref, label="Ecut=100", color=2)
    Example block output

    The problem of non-smoothness of the approximated energy is typically avoided by taking a large enough Ecut, at the cost of a high computation time. Another method consist in introducing a modified kinetic term defined through the data of a blow-up function, a method which is also referred to as "energy cutoff smearing". DFTK features energy cutoff smearing using the CHV blow-up function introduced in [CHV2022] that is mathematically ensured to provide $C^2$ regularity of the energy bands.

    Éric Cancès, Muhammad Hassan and Laurent Vidal Modified-operator method for the calculation of band diagrams of crystalline materials, 2022. arXiv preprint.

    Let us lauch the computation again with the modified kinetic term.

    E0_modified = compute_ground_state_energy.(a_list; kinetic_blowup=BlowupCHV(), Ecut, kgrid);
    Abinit energy cutoff smearing option

    For the sake of completeness, DFTK also provides the blow-up function BlowupAbinit proposed in the Abinit quantum chemistry code. This function depends on a parameter Ecutsm fixed by the user (see Abinit user guide). For the right choice of Ecutsm, BlowupAbinit corresponds to the BlowupCHV approach with coefficients ensuring $C^1$ regularity. To choose BlowupAbinit, pass kinetic_blowup=BlowupAbinit(Ecutsm) to the model constructors.

    We can know compare the approximation of the energy as well as the estimated lattice constant for each strategy.

    estimate_a0(E0_values) = a_list[findmin(E0_values)[2]]
     a0_naive, a0_ref, a0_modified = estimate_a0.([E0_naive, E0_ref, E0_modified])
     
     shift = mean(abs.(E0_modified .- E0_ref))  # Shift for legibility of the plot
    @@ -39,5 +39,5 @@
     vline!(p, [a0], label="experimental a0", linestyle=:dash, linecolor=:black)
     vline!(p, [a0_naive], label="a0 Ecut=5", linestyle=:dash, color=1)
     vline!(p, [a0_ref], label="a0 Ecut=100", linestyle=:dash, color=2)
    -vline!(p, [a0_modified], label="a0 Ecut=5 + BlowupCHV", linestyle=:dash, color=3)
    Example block output

    The smoothed curve obtained with the modified kinetic term allow to clearly designate a minimal value of the energy with respect to the lattice parameter $a$, even with the low Ecut=5 Ha.

    println("Error of approximation of the reference a0 with modified kinetic term:"*
    -        " $(round((a0_modified - a0_ref)*100/a0_ref, digits=5))%")
    Error of approximation of the reference a0 with modified kinetic term: 0.50393%
    +vline!(p, [a0_modified], label="a0 Ecut=5 + BlowupCHV", linestyle=:dash, color=3)Example block output

    The smoothed curve obtained with the modified kinetic term allow to clearly designate a minimal value of the energy with respect to the lattice parameter $a$, even with the low Ecut=5 Ha.

    println("Error of approximation of the reference a0 with modified kinetic term:"*
    +        " $(round((a0_modified - a0_ref)*100/a0_ref, digits=5))%")
    Error of approximation of the reference a0 with modified kinetic term: 0.50393%
    diff --git a/dev/examples/error_estimates_forces.ipynb b/dev/examples/error_estimates_forces.ipynb index fea0d6f914..d2d4ff8c52 100644 --- a/dev/examples/error_estimates_forces.ipynb +++ b/dev/examples/error_estimates_forces.ipynb @@ -499,10 +499,10 @@ "name": "stdout", "output_type": "stream", "text": [ - " F(P_*) = [1.47893, -1.25370, 0.81009] (rel. error: 0.00000)\n", - " F(P) = [1.13548, -1.01532, 0.40016] (rel. error: 0.20481)\n", - " F(P) - df(P)⋅Rschur(P) = [1.29128, -1.10179, 0.69055] (rel. error: 0.07830)\n", - " F(P) - df(P)⋅(P-P_*) = [1.50900, -1.28630, 0.86146] (rel. error: 0.08072)\n" + " F(P_*) = [1.47892, -1.25374, 0.81010] (rel. error: 0.00000)\n", + " F(P) = [1.13545, -1.01532, 0.40013] (rel. error: 0.20484)\n", + " F(P) - df(P)⋅Rschur(P) = [1.29125, -1.10179, 0.69055] (rel. error: 0.07832)\n", + " F(P) - df(P)⋅(P-P_*) = [1.50895, -1.28635, 0.86145] (rel. error: 0.08072)\n" ] } ], diff --git a/dev/examples/error_estimates_forces/index.html b/dev/examples/error_estimates_forces/index.html index ca12d5fa16..1a2217b492 100644 --- a/dev/examples/error_estimates_forces/index.html +++ b/dev/examples/error_estimates_forces/index.html @@ -108,7 +108,7 @@ relerror["F(P) - df(P)⋅Rschur(P)"] = compute_relerror(f - df_schur);

    Summary of all forces on the first atom (Ti)

    for (key, value) in pairs(forces)
         @printf("%30s = [%7.5f, %7.5f, %7.5f]   (rel. error: %7.5f)\n",
                 key, (value[1])..., relerror[key])
    -end
                            F(P_*) = [1.47898, -1.25376, 0.81010]   (rel. error: 0.00000)
    -                          F(P) = [1.13551, -1.01533, 0.40016]   (rel. error: 0.20481)
    -        F(P) - df(P)⋅Rschur(P) = [1.29125, -1.10180, 0.69055]   (rel. error: 0.07832)
    -          F(P) - df(P)⋅(P-P_*) = [1.50903, -1.28635, 0.86145]   (rel. error: 0.08072)

    Notice how close the computable expression $F(P) - {\rm d}F(P)⋅R_{\rm Schur}(P)$ is to the best linearization ansatz $F(P) - {\rm d}F(P)⋅(P-P_*)$.

    • CDKL2021E. Cancès, G. Dusson, G. Kemlin, and A. Levitt Practical error bounds for properties in plane-wave electronic structure calculations Preprint, 2021. arXiv
    +end
                            F(P_*) = [1.47898, -1.25375, 0.81011]   (rel. error: 0.00000)
    +                          F(P) = [1.13545, -1.01524, 0.40015]   (rel. error: 0.20483)
    +        F(P) - df(P)⋅Rschur(P) = [1.29133, -1.10187, 0.69055]   (rel. error: 0.07830)
    +          F(P) - df(P)⋅(P-P_*) = [1.50903, -1.28633, 0.86146]   (rel. error: 0.08072)

    Notice how close the computable expression $F(P) - {\rm d}F(P)⋅R_{\rm Schur}(P)$ is to the best linearization ansatz $F(P) - {\rm d}F(P)⋅(P-P_*)$.

    • CDKL2021E. Cancès, G. Dusson, G. Kemlin, and A. Levitt Practical error bounds for properties in plane-wave electronic structure calculations Preprint, 2021. arXiv
    diff --git a/dev/examples/forwarddiff.ipynb b/dev/examples/forwarddiff.ipynb index 22b9dff08c..48aac41630 100644 --- a/dev/examples/forwarddiff.ipynb +++ b/dev/examples/forwarddiff.ipynb @@ -67,39 +67,37 @@ "text": [ "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -2.770923774427 -0.52 9.0 \n", - " 2 -2.772147774115 -2.91 -1.32 1.0 105ms\n", - " 3 -2.772170358956 -4.65 -2.50 1.0 119ms\n", - " 4 -2.772170650575 -6.54 -3.22 1.0 106ms\n", - " 5 -2.772170722152 -7.15 -3.97 2.0 123ms\n", - " 6 -2.772170722664 -9.29 -4.24 1.0 124ms\n", - " 7 -2.772170723004 -9.47 -5.07 1.0 112ms\n", - " 8 -2.772170723010 -11.21 -5.12 1.0 125ms\n", - " 9 -2.772170723014 -11.41 -5.64 1.0 116ms\n", - " 10 -2.772170723015 -12.30 -5.83 2.0 147ms\n", - " 11 -2.772170723015 -12.76 -6.04 1.0 126ms\n", - " 12 -2.772170723015 -13.20 -6.32 1.0 125ms\n", - " 13 -2.772170723015 -13.94 -6.64 1.0 133ms\n", - " 14 -2.772170723015 + -15.35 -7.98 1.0 125ms\n", - " 15 -2.772170723015 -13.64 -7.21 2.0 159ms\n", - " 16 -2.772170723015 + -14.40 -8.20 2.0 140ms\n", + " 1 -2.770839461338 -0.52 9.0 \n", + " 2 -2.772146254115 -2.88 -1.33 1.0 201ms\n", + " 3 -2.772170093618 -4.62 -2.40 1.0 234ms\n", + " 4 -2.772170637219 -6.26 -3.10 1.0 207ms\n", + " 5 -2.772170722632 -7.07 -4.39 2.0 294ms\n", + " 6 -2.772170722871 -9.62 -4.61 1.0 204ms\n", + " 7 -2.772170723006 -9.87 -5.16 2.0 242ms\n", + " 8 -2.772170723013 -11.14 -5.56 1.0 228ms\n", + " 9 -2.772170723015 -11.77 -6.35 2.0 247ms\n", + " 10 -2.772170723015 -13.75 -6.77 1.0 282ms\n", + " 11 -2.772170723015 -13.94 -7.72 1.0 235ms\n", + " 12 -2.772170723015 -14.88 -8.45 2.0 287ms\n", "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -2.770706289832 -0.53 9.0 \n", - " 2 -2.772049816052 -2.87 -1.30 1.0 130ms\n", - " 3 -2.772082415237 -4.49 -2.64 1.0 105ms\n", - " 4 -2.772083416089 -6.00 -4.04 2.0 155ms\n", - " 5 -2.772083417782 -8.77 -4.83 2.0 122ms\n", - " 6 -2.772083417809 -10.57 -6.09 1.0 117ms\n", - " 7 -2.772083417811 -11.90 -6.28 2.0 126ms\n", - " 8 -2.772083417811 -13.56 -7.29 1.0 115ms\n", - " 9 -2.772083417811 + -Inf -8.04 2.0 143ms\n" + " 1 -2.770694186904 -0.53 8.0 \n", + " 2 -2.772051955175 -2.87 -1.31 1.0 219ms\n", + " 3 -2.772082745661 -4.51 -2.55 1.0 189ms\n", + " 4 -2.772083408311 -6.18 -3.66 2.0 242ms\n", + " 5 -2.772083416625 -8.08 -3.97 2.0 261ms\n", + " 6 -2.772083417774 -8.94 -4.93 1.0 205ms\n", + " 7 -2.772083417810 -10.44 -5.87 2.0 248ms\n", + " 8 -2.772083417811 -12.97 -6.43 1.0 231ms\n", + " 9 -2.772083417811 -13.47 -7.12 2.0 268ms\n", + " 10 -2.772083417811 + -14.40 -7.75 1.0 230ms\n", + " 11 -2.772083417811 -14.65 -8.59 2.0 256ms\n" ] }, { "output_type": "execute_result", "data": { - "text/plain": "1.7735579492840987" + "text/plain": "1.7735581436821426" }, "metadata": {}, "execution_count": 2 @@ -132,19 +130,20 @@ "text": [ "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -2.770740251334 -0.52 9.0 \n", - " 2 -2.772059740510 -2.88 -1.32 1.0 103ms\n", - " 3 -2.772082965889 -4.63 -2.42 1.0 130ms\n", - " 4 -2.772083328926 -6.44 -3.11 1.0 126ms\n", - " 5 -2.772083417630 -7.05 -4.33 2.0 171ms\n", - " 6 -2.772083417738 -9.97 -4.63 1.0 109ms\n", - " 7 -2.772083417809 -10.15 -5.78 1.0 112ms\n", - " 8 -2.772083417811 -11.81 -6.47 2.0 137ms\n", - " 9 -2.772083417811 + -13.97 -6.57 2.0 126ms\n", - " 10 -2.772083417811 -13.85 -8.23 1.0 129ms\n", + " 1 -2.770756389490 -0.52 9.0 \n", + " 2 -2.772060138740 -2.88 -1.32 1.0 189ms\n", + " 3 -2.772082987809 -4.64 -2.43 1.0 272ms\n", + " 4 -2.772083336006 -6.46 -3.12 1.0 197ms\n", + " 5 -2.772083417615 -7.09 -4.32 2.0 232ms\n", + " 6 -2.772083417743 -9.89 -4.67 1.0 267ms\n", + " 7 -2.772083417809 -10.18 -5.74 1.0 211ms\n", + " 8 -2.772083417811 -11.72 -6.73 2.0 285ms\n", + " 9 -2.772083417811 -14.15 -6.67 1.0 229ms\n", + " 10 -2.772083417811 + -14.88 -7.91 1.0 248ms\n", + " 11 -2.772083417811 + -14.57 -8.34 2.0 283ms\n", "\n", - "Polarizability via ForwardDiff: 1.7725349591124775\n", - "Polarizability via finite difference: 1.7735579492840987\n" + "Polarizability via ForwardDiff: 1.7725349777181945\n", + "Polarizability via finite difference: 1.7735581436821426\n" ] } ], diff --git a/dev/examples/forwarddiff/index.html b/dev/examples/forwarddiff/index.html index 82b5176c6f..4b7f005ad9 100644 --- a/dev/examples/forwarddiff/index.html +++ b/dev/examples/forwarddiff/index.html @@ -32,22 +32,21 @@ end;

    With this in place we can compute the polarizability from finite differences (just like in the previous example):

    polarizability_fd = let
         ε = 0.01
         (compute_dipole(ε) - compute_dipole(0.0)) / ε
    -end
    1.77355810989668

    We do the same thing using automatic differentiation. Under the hood this uses custom rules to implicitly differentiate through the self-consistent field fixed-point problem.

    polarizability = ForwardDiff.derivative(compute_dipole, 0.0)
    +end
    1.7735580712755261

    We do the same thing using automatic differentiation. Under the hood this uses custom rules to implicitly differentiate through the self-consistent field fixed-point problem.

    polarizability = ForwardDiff.derivative(compute_dipole, 0.0)
     println()
     println("Polarizability via ForwardDiff:       $polarizability")
     println("Polarizability via finite difference: $polarizability_fd")
    n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime
     ---   ---------------   ---------   ---------   ----   ------
    -  1   -2.770746717108                   -0.53    8.0
    -  2   -2.772048749051       -2.89       -1.31    1.0    105ms
    -  3   -2.772082236488       -4.48       -2.63    1.0    112ms
    -  4   -2.772083415073       -5.93       -4.01    2.0    165ms
    -  5   -2.772083417778       -8.57       -4.64    2.0    123ms
    -  6   -2.772083417808      -10.51       -5.47    1.0    134ms
    -  7   -2.772083417811      -11.66       -6.17    2.0    127ms
    -  8   -2.772083417811      -13.41       -6.77    1.0    196ms
    -  9   -2.772083417811      -14.65       -7.42    2.0    127ms
    - 10   -2.772083417811   +  -15.35       -7.87    1.0    139ms
    - 11   -2.772083417811      -13.95       -9.26    1.0    116ms
    +  1   -2.770646945410                   -0.53    8.0
    +  2   -2.772048429561       -2.85       -1.30    1.0    179ms
    +  3   -2.772082426503       -4.47       -2.66    1.0    197ms
    +  4   -2.772083414249       -6.01       -3.88    2.0    322ms
    +  5   -2.772083417601       -8.47       -4.36    2.0    215ms
    +  6   -2.772083417803       -9.70       -5.47    1.0    246ms
    +  7   -2.772083417810      -11.12       -5.96    2.0    221ms
    +  8   -2.772083417811      -12.79       -6.88    2.0    258ms
    +  9   -2.772083417811      -14.15       -7.56    1.0    220ms
    + 10   -2.772083417811   +  -13.89       -8.64    2.0    269ms
     
    -Polarizability via ForwardDiff:       1.7725349717766634
    -Polarizability via finite difference: 1.77355810989668
    +Polarizability via ForwardDiff: 1.7725349721335355 +Polarizability via finite difference: 1.7735580712755261 diff --git a/dev/examples/gaas_surface.ipynb b/dev/examples/gaas_surface.ipynb index 6b789ab687..7c73b7a814 100644 --- a/dev/examples/gaas_surface.ipynb +++ b/dev/examples/gaas_surface.ipynb @@ -161,19 +161,17 @@ "text": [ "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -16.58817215651 -0.58 5.2 \n", - " 2 -16.72528948283 -0.86 -1.01 1.0 243ms\n", - " 3 -16.73056991712 -2.28 -1.57 2.2 332ms\n", - " 4 -16.73127990746 -3.15 -2.17 2.0 287ms\n", - " 5 -16.73133343948 -4.27 -2.61 2.2 330ms\n", - " 6 -16.73133595891 -5.60 -2.95 1.3 237ms\n", - " 7 -16.73105170951 + -3.55 -2.58 2.2 330ms\n", - " 8 -16.73132799606 -3.56 -3.14 2.4 304ms\n", - " 9 -16.73133073428 -5.56 -3.27 2.4 325ms\n", - " 10 -16.73133895740 -5.08 -3.75 2.1 314ms\n", - " 11 -16.73133975880 -6.10 -3.94 2.4 336ms\n", - " 12 -16.73133981459 -7.25 -4.00 2.8 347ms\n", - " 13 -16.73134019651 -6.42 -4.89 1.9 272ms\n" + " 1 -16.58816551454 -0.58 5.3 \n", + " 2 -16.72531977206 -0.86 -1.01 1.0 550ms\n", + " 3 -16.73056124805 -2.28 -1.57 2.3 773ms\n", + " 4 -16.73127155939 -3.15 -2.16 2.0 630ms\n", + " 5 -16.73133316929 -4.21 -2.61 2.0 710ms\n", + " 6 -16.73133578701 -5.58 -2.95 1.3 526ms\n", + " 7 -16.73105634894 + -3.55 -2.58 2.1 734ms\n", + " 8 -16.73132414416 -3.57 -3.10 2.4 684ms\n", + " 9 -16.73133025777 -5.21 -3.26 2.4 720ms\n", + " 10 -16.73133972134 -5.02 -3.93 2.1 724ms\n", + " 11 -16.73134014294 -6.38 -4.31 2.0 695ms\n" ] } ], @@ -193,7 +191,7 @@ { "output_type": "execute_result", "data": { - "text/plain": "Energy breakdown (in Ha):\n Kinetic 5.8594123 \n AtomicLocal -105.6102672\n AtomicNonlocal 2.3494873 \n Ewald 35.5044300\n PspCorrection 0.2016043 \n Hartree 49.5616674\n Xc -4.5976708\n Entropy -0.0000035\n\n total -16.731340196511" + "text/plain": "Energy breakdown (in Ha):\n Kinetic 5.8594378 \n AtomicLocal -105.6106122\n AtomicNonlocal 2.3494996 \n Ewald 35.5044300\n PspCorrection 0.2016043 \n Hartree 49.5619877\n Xc -4.5976839\n Entropy -0.0000035\n\n total -16.731340142943" }, "metadata": {}, "execution_count": 7 diff --git a/dev/examples/gaas_surface/index.html b/dev/examples/gaas_surface/index.html index 021f6e2b54..f4611a371a 100644 --- a/dev/examples/gaas_surface/index.html +++ b/dev/examples/gaas_surface/index.html @@ -56,25 +56,23 @@ scfres = self_consistent_field(basis, tol=1e-4, mixing=LdosMixing());
    n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime
     ---   ---------------   ---------   ---------   ----   ------
    -  1   -16.58705454513                   -0.58    5.2
    -  2   -16.72535851774       -0.86       -1.01    1.0    243ms
    -  3   -16.73056680887       -2.28       -1.57    2.3    325ms
    -  4   -16.73127761664       -3.15       -2.16    1.9    312ms
    -  5   -16.73133262166       -4.26       -2.61    2.0    294ms
    -  6   -16.73133456747       -5.71       -2.95    1.6    260ms
    -  7   -16.73097083231   +   -3.44       -2.52    2.2    316ms
    -  8   -16.73133511450       -3.44       -3.24    2.3    346ms
    -  9   -16.73133663774       -5.82       -3.44    2.2    309ms
    - 10   -16.73133772876       -5.96       -3.60    2.6    328ms
    - 11   -16.73133869706       -6.01       -3.69    2.3    329ms
    - 12   -16.73134014967       -5.84       -4.42    2.6    337ms
    scfres.energies
    Energy breakdown (in Ha):
    -    Kinetic             5.8593752 
    -    AtomicLocal         -105.6098310
    -    AtomicNonlocal      2.3494697 
    +  1   -16.58822901408                   -0.58    5.2
    +  2   -16.72521261077       -0.86       -1.01    1.0    567ms
    +  3   -16.73057898784       -2.27       -1.57    1.9    586ms
    +  4   -16.73128307105       -3.15       -2.17    2.0    672ms
    +  5   -16.73133253787       -4.31       -2.60    1.9    616ms
    +  6   -16.73133396014       -5.85       -2.94    1.6    541ms
    +  7   -16.73088808092   +   -3.35       -2.48    2.3    681ms
    +  8   -16.73133314830       -3.35       -3.26    2.3    638ms
    +  9   -16.73133922001       -5.22       -3.63    2.3    705ms
    + 10   -16.73134017895       -6.02       -4.31    2.0    645ms
    scfres.energies
    Energy breakdown (in Ha):
    +    Kinetic             5.8594155 
    +    AtomicLocal         -105.6109070
    +    AtomicNonlocal      2.3494782 
         Ewald               35.5044300
         PspCorrection       0.2016043 
    -    Hartree             49.5612660
    -    Xc                  -4.5976509
    +    Hartree             49.5623161
    +    Xc                  -4.5976739
         Entropy             -0.0000035
     
    -    total               -16.731340149673
    + total -16.731340178948 diff --git a/dev/examples/geometry_optimization.ipynb b/dev/examples/geometry_optimization.ipynb index 6ac9c74c30..4a362d0e2c 100644 --- a/dev/examples/geometry_optimization.ipynb +++ b/dev/examples/geometry_optimization.ipynb @@ -130,15 +130,15 @@ "text": [ "Iter Function value Gradient norm \n", " 0 -1.061651e+00 6.219313e-01\n", - " * time: 4.696846008300781e-5\n", - " 1 -1.064073e+00 2.919806e-01\n", - " * time: 2.5291919708251953\n", - " 2 -1.066008e+00 4.821008e-02\n", - " * time: 3.1288928985595703\n", - " 3 -1.066049e+00 4.314751e-04\n", - " * time: 3.501175880432129\n", - " 4 -1.066049e+00 6.644389e-09\n", - " * time: 3.7457048892974854\n", + " * time: 0.00010013580322265625\n", + " 1 -1.064073e+00 2.919805e-01\n", + " * time: 5.303492069244385\n", + " 2 -1.066008e+00 4.821004e-02\n", + " * time: 6.561645030975342\n", + " 3 -1.066049e+00 4.314744e-04\n", + " * time: 7.293626070022583\n", + " 4 -1.066049e+00 6.644386e-09\n", + " * time: 7.79390811920166\n", "\n", "Optimal bond length for Ecut=5.00: 1.556 Bohr\n" ] diff --git a/dev/examples/geometry_optimization/index.html b/dev/examples/geometry_optimization/index.html index be2c0148e5..f07e035914 100644 --- a/dev/examples/geometry_optimization/index.html +++ b/dev/examples/geometry_optimization/index.html @@ -37,14 +37,14 @@ dmin = norm(lattice*xmin[1:3] - lattice*xmin[4:6]) @printf "\nOptimal bond length for Ecut=%.2f: %.3f Bohr\n" Ecut dmin
    Iter     Function value   Gradient norm
          0    -1.061651e+00     6.219313e-01
    - * time: 4.696846008300781e-5
    -     1    -1.064073e+00     2.919811e-01
    - * time: 0.8912630081176758
    -     2    -1.066008e+00     4.821027e-02
    - * time: 1.5290889739990234
    -     3    -1.066049e+00     4.314798e-04
    - * time: 1.9179770946502686
    -     4    -1.066049e+00     6.644489e-09
    - * time: 2.158687114715576
    + * time: 0.0001049041748046875
    +     1    -1.064073e+00     2.919809e-01
    + * time: 1.6109528541564941
    +     2    -1.066008e+00     4.821021e-02
    + * time: 2.725564956665039
    +     3    -1.066049e+00     4.314782e-04
    + * time: 3.414324998855591
    +     4    -1.066049e+00     6.644457e-09
    + * time: 3.834296941757202
     
    -Optimal bond length for Ecut=5.00: 1.556 Bohr

    We used here very rough parameters to generate the example and setting Ecut to 10 Ha yields a bond length of 1.523 Bohr, which agrees with ABINIT.

    Degrees of freedom

    We used here a very general setting where we optimized on the 6 variables representing the position of the 2 atoms and it can be easily extended to molecules with more atoms (such as $H_2O$). In the particular case of $H_2$, we could use only the internal degree of freedom which, in this case, is just the bond length.

    +Optimal bond length for Ecut=5.00: 1.556 Bohr

    We used here very rough parameters to generate the example and setting Ecut to 10 Ha yields a bond length of 1.523 Bohr, which agrees with ABINIT.

    Degrees of freedom

    We used here a very general setting where we optimized on the 6 variables representing the position of the 2 atoms and it can be easily extended to molecules with more atoms (such as $H_2O$). In the particular case of $H_2$, we could use only the internal degree of freedom which, in this case, is just the bond length.

    diff --git a/dev/examples/graphene.ipynb b/dev/examples/graphene.ipynb index 1942582ec3..4d76c00770 100644 --- a/dev/examples/graphene.ipynb +++ b/dev/examples/graphene.ipynb @@ -24,300 +24,300 @@ "text": [ "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -11.15665511901 -0.60 5.9 \n", - " 2 -11.16018401064 -2.45 -1.30 1.0 140ms\n", - " 3 -11.16039634701 -3.67 -2.33 2.0 139ms\n", - " 4 -11.16041672123 -4.69 -3.27 3.0 179ms\n", - " 5 -11.16041704641 -6.49 -3.47 3.0 170ms\n", - " 6 -11.16041704993 -8.45 -3.63 1.4 122ms\n", - " 7 -11.16041705093 -9.00 -3.90 1.7 137ms\n", - " 8 -11.16041705126 -9.48 -4.33 2.0 133ms\n", - " 9 -11.16041705137 -9.96 -4.66 1.9 136ms\n", - " 10 -11.16041705141 -10.36 -4.95 2.0 140ms\n", - " 11 -11.16041705144 -10.53 -5.34 2.6 163ms\n", - " 12 -11.16041705145 -11.11 -5.59 3.0 181ms\n", - " 13 -11.16041705145 -11.75 -6.10 2.3 157ms\n", + " 1 -11.15653632143 -0.60 6.0 \n", + " 2 -11.16015461829 -2.44 -1.30 1.0 251ms\n", + " 3 -11.16039351378 -3.62 -2.33 2.0 298ms\n", + " 4 -11.16041652742 -4.64 -3.22 2.7 360ms\n", + " 5 -11.16041704348 -6.29 -3.44 2.9 343ms\n", + " 6 -11.16041704953 -8.22 -3.59 1.9 308ms\n", + " 7 -11.16041705093 -8.85 -3.87 1.6 269ms\n", + " 8 -11.16041705127 -9.46 -4.29 1.9 280ms\n", + " 9 -11.16041705137 -10.03 -4.67 2.1 304ms\n", + " 10 -11.16041705141 -10.37 -4.98 2.3 305ms\n", + " 11 -11.16041705144 -10.58 -5.26 2.6 337ms\n", + " 12 -11.16041705145 -11.00 -5.61 2.9 368ms\n", + " 13 -11.16041705145 -11.61 -6.06 2.7 354ms\n", "Computing bands along kpath:\n", " Γ -> M -> K -> Γ\n", - "\rDiagonalising Hamiltonian kblocks: 5%|▊ | ETA: 0:00:06\u001b[K\rDiagonalising Hamiltonian kblocks: 10%|█▌ | ETA: 0:00:04\u001b[K\rDiagonalising Hamiltonian kblocks: 15%|██▍ | ETA: 0:00:03\u001b[K\rDiagonalising Hamiltonian kblocks: 20%|███▏ | ETA: 0:00:03\u001b[K\rDiagonalising Hamiltonian kblocks: 24%|███▉ | ETA: 0:00:03\u001b[K\rDiagonalising Hamiltonian kblocks: 29%|████▋ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 34%|█████▌ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 39%|██████▎ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 44%|███████ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 49%|███████▊ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 54%|████████▋ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 59%|█████████▍ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 66%|██████████▌ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 71%|███████████▍ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 76%|████████████▏ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 80%|████████████▉ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 85%|█████████████▋ | ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 90%|██████████████▌ | ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 95%|███████████████▎| ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 100%|████████████████| Time: 0:00:02\u001b[K\n" + "\rDiagonalising Hamiltonian kblocks: 5%|▊ | ETA: 0:00:13\u001b[K\rDiagonalising Hamiltonian kblocks: 7%|█▏ | ETA: 0:00:10\u001b[K\rDiagonalising Hamiltonian kblocks: 10%|█▌ | ETA: 0:00:09\u001b[K\rDiagonalising Hamiltonian kblocks: 12%|██ | ETA: 0:00:08\u001b[K\rDiagonalising Hamiltonian kblocks: 15%|██▍ | ETA: 0:00:08\u001b[K\rDiagonalising Hamiltonian kblocks: 17%|██▊ | ETA: 0:00:07\u001b[K\rDiagonalising Hamiltonian kblocks: 20%|███▏ | ETA: 0:00:07\u001b[K\rDiagonalising Hamiltonian kblocks: 22%|███▌ | ETA: 0:00:06\u001b[K\rDiagonalising Hamiltonian kblocks: 24%|███▉ | ETA: 0:00:06\u001b[K\rDiagonalising Hamiltonian kblocks: 27%|████▎ | ETA: 0:00:06\u001b[K\rDiagonalising Hamiltonian kblocks: 29%|████▋ | ETA: 0:00:05\u001b[K\rDiagonalising Hamiltonian kblocks: 32%|█████▏ | ETA: 0:00:05\u001b[K\rDiagonalising Hamiltonian kblocks: 34%|█████▌ | ETA: 0:00:05\u001b[K\rDiagonalising Hamiltonian kblocks: 37%|█████▉ | ETA: 0:00:05\u001b[K\rDiagonalising Hamiltonian kblocks: 39%|██████▎ | ETA: 0:00:04\u001b[K\rDiagonalising Hamiltonian kblocks: 41%|██████▋ | ETA: 0:00:04\u001b[K\rDiagonalising Hamiltonian kblocks: 44%|███████ | ETA: 0:00:04\u001b[K\rDiagonalising Hamiltonian kblocks: 46%|███████▍ | ETA: 0:00:04\u001b[K\rDiagonalising Hamiltonian kblocks: 49%|███████▊ | ETA: 0:00:04\u001b[K\rDiagonalising Hamiltonian kblocks: 51%|████████▎ | ETA: 0:00:03\u001b[K\rDiagonalising Hamiltonian kblocks: 54%|████████▋ | ETA: 0:00:03\u001b[K\rDiagonalising Hamiltonian kblocks: 56%|█████████ | ETA: 0:00:03\u001b[K\rDiagonalising Hamiltonian kblocks: 59%|█████████▍ | ETA: 0:00:03\u001b[K\rDiagonalising Hamiltonian kblocks: 61%|█████████▊ | ETA: 0:00:03\u001b[K\rDiagonalising Hamiltonian kblocks: 63%|██████████▏ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 66%|██████████▌ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 68%|██████████▉ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 71%|███████████▍ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 73%|███████████▊ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 76%|████████████▏ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 78%|████████████▌ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 80%|████████████▉ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 83%|█████████████▎ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 85%|█████████████▋ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 88%|██████████████ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 90%|██████████████▌ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 93%|██████████████▉ | ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 95%|███████████████▎| ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 98%|███████████████▋| ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 100%|████████████████| Time: 0:00:06\u001b[K\n" ] }, { "output_type": "execute_result", "data": { "text/plain": "Plot{Plots.GRBackend() n=25}", - "image/png": 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", + "image/png": 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b841483bda..0f91ac96a4 100644 --- a/dev/examples/graphene/index.html +++ b/dev/examples/graphene/index.html @@ -31,4 +31,4 @@ # Construct 2D path through Brillouin zone sgnum = 13 # Graphene space group number kpath = irrfbz_path(model; dim=2, sgnum) -plot_bandstructure(scfres, kpath; kline_density=20)Example block output +plot_bandstructure(scfres, kpath; kline_density=20)Example block output diff --git a/dev/examples/gross_pitaevskii.ipynb b/dev/examples/gross_pitaevskii.ipynb index 2b260a774b..d7a1da7c06 100644 --- a/dev/examples/gross_pitaevskii.ipynb +++ b/dev/examples/gross_pitaevskii.ipynb @@ -128,112 +128,110 @@ "output_type": "stream", "text": [ "Iter Function value Gradient norm \n", - " 0 1.944051e+02 1.823480e+02\n", - " * time: 0.0004150867462158203\n", - " 1 1.850890e+02 1.286173e+02\n", - " * time: 0.0011169910430908203\n", - " 2 1.832678e+02 1.402006e+02\n", - " * time: 0.002583026885986328\n", - " 3 1.401305e+02 1.338318e+02\n", - " * time: 0.004455089569091797\n", - " 4 4.853992e+01 9.786949e+01\n", - " * time: 0.0065500736236572266\n", - " 5 1.520903e+01 1.530864e+01\n", - " * time: 0.008407115936279297\n", - " 6 1.077606e+01 4.271738e+01\n", - " * time: 0.009639978408813477\n", - " 7 8.489203e+00 1.189012e+01\n", - " * time: 0.010888099670410156\n", - " 8 3.338702e+00 1.109531e+01\n", - " * time: 0.01212000846862793\n", - " 9 2.059478e+00 4.200249e+00\n", - " * time: 0.013146162033081055\n", - " 10 1.660867e+00 3.249878e+00\n", - " * time: 0.014155149459838867\n", - " 11 1.582444e+00 3.877356e+00\n", - " * time: 0.014944076538085938\n", - " 12 1.312009e+00 2.575918e+00\n", - " * time: 0.015725135803222656\n", - " 13 1.254087e+00 2.357784e+00\n", - " * time: 0.016503095626831055\n", - " 14 1.200906e+00 1.428375e+00\n", - " * time: 0.017302989959716797\n", - " 15 1.173507e+00 1.157114e+00\n", - " * time: 0.018080949783325195\n", - " 16 1.155699e+00 9.599644e-01\n", - " * time: 0.0188601016998291\n", - " 17 1.152735e+00 3.779703e-01\n", - " * time: 0.019421100616455078\n", - " 18 1.147113e+00 1.545185e-01\n", - " * time: 0.019968032836914062\n", - " 19 1.146226e+00 2.548289e-01\n", - " * time: 0.02051711082458496\n", - " 20 1.145057e+00 9.984479e-02\n", - " * time: 0.02108907699584961\n", - " 21 1.144436e+00 6.783558e-02\n", - " * time: 0.021863937377929688\n", - " 22 1.144328e+00 7.088421e-02\n", - " * time: 0.022639989852905273\n", - " 23 1.144213e+00 4.586109e-02\n", - " * time: 0.023188114166259766\n", - " 24 1.144134e+00 3.741282e-02\n", - " * time: 0.023962020874023438\n", - " 25 1.144099e+00 2.846996e-02\n", - " * time: 0.02476811408996582\n", - " 26 1.144043e+00 7.410815e-03\n", - " * time: 0.025563955307006836\n", - " 27 1.144040e+00 4.610236e-03\n", - " * time: 0.026340007781982422\n", - " 28 1.144038e+00 3.362236e-03\n", - " * time: 0.02711796760559082\n", - " 29 1.144038e+00 1.820775e-03\n", - " * time: 0.02766704559326172\n", - " 30 1.144037e+00 1.262467e-03\n", - " * time: 0.028213977813720703\n", - " 31 1.144037e+00 9.656137e-04\n", - " * time: 0.029015064239501953\n", - " 32 1.144037e+00 3.758081e-04\n", - " * time: 0.02979111671447754\n", - " 33 1.144037e+00 3.316569e-04\n", - " * time: 0.030571937561035156\n", - " 34 1.144037e+00 2.586165e-04\n", - " * time: 0.031343936920166016\n", - " 35 1.144037e+00 1.848972e-04\n", - " * time: 0.03212094306945801\n", - " 36 1.144037e+00 1.503217e-04\n", - " * time: 0.03292512893676758\n", - " 37 1.144037e+00 1.298954e-04\n", - " * time: 0.03370094299316406\n", - " 38 1.144037e+00 9.837533e-05\n", - " * time: 0.034475088119506836\n", - " 39 1.144037e+00 6.399532e-05\n", - " * time: 0.035256147384643555\n", - " 40 1.144037e+00 4.435641e-05\n", - " * time: 0.036028146743774414\n", - " 41 1.144037e+00 2.453920e-05\n", - " * time: 0.03682994842529297\n", - " 42 1.144037e+00 5.708565e-06\n", - " * time: 0.03737807273864746\n", - " 43 1.144037e+00 2.865231e-06\n", - " * time: 0.03792595863342285\n", - " 44 1.144037e+00 2.491957e-06\n", - " * time: 0.03870511054992676\n", - " 45 1.144037e+00 2.262987e-06\n", - " * time: 0.03948211669921875\n", - " 46 1.144037e+00 8.247608e-07\n", - " * time: 0.0402531623840332\n", - " 47 1.144037e+00 5.105787e-07\n", - " * time: 0.040821075439453125\n", - " 48 1.144037e+00 3.798547e-07\n", - " * time: 0.04159712791442871\n", - " 49 1.144037e+00 2.667230e-07\n", - " * time: 0.042368173599243164\n", - " 50 1.144037e+00 1.789829e-07\n", - " * time: 0.04314017295837402\n", - " 51 1.144037e+00 1.169507e-07\n", - " * time: 0.04391908645629883\n", - " 52 1.144037e+00 1.000921e-07\n", - " * time: 0.044715166091918945\n" + " 0 1.602133e+02 1.291349e+02\n", + " * time: 0.0006830692291259766\n", + " 1 1.566766e+02 1.224915e+02\n", + " * time: 0.002835988998413086\n", + " 2 1.127753e+02 1.406216e+02\n", + " * time: 0.0056591033935546875\n", + " 3 7.112934e+01 1.087806e+02\n", + " * time: 0.008722066879272461\n", + " 4 1.296166e+01 3.235395e+01\n", + " * time: 0.012138128280639648\n", + " 5 1.032506e+01 1.942554e+01\n", + " * time: 0.014579057693481445\n", + " 6 9.148736e+00 1.031390e+01\n", + " * time: 0.016012191772460938\n", + " 7 6.757129e+00 1.048656e+01\n", + " * time: 0.0183560848236084\n", + " 8 2.225645e+00 3.314535e+00\n", + " * time: 0.020933151245117188\n", + " 9 1.519855e+00 3.052543e+00\n", + " * time: 0.023130178451538086\n", + " 10 1.398376e+00 4.537444e+00\n", + " * time: 0.024669170379638672\n", + " 11 1.295186e+00 1.690004e+00\n", + " * time: 0.026125192642211914\n", + " 12 1.231199e+00 9.583212e-01\n", + " * time: 0.02758502960205078\n", + " 13 1.185412e+00 6.639060e-01\n", + " * time: 0.02904820442199707\n", + " 14 1.168090e+00 7.323688e-01\n", + " * time: 0.030494213104248047\n", + " 15 1.150276e+00 2.017267e-01\n", + " * time: 0.031980037689208984\n", + " 16 1.146232e+00 1.226820e-01\n", + " * time: 0.033724069595336914\n", + " 17 1.145113e+00 9.688310e-02\n", + " * time: 0.0353240966796875\n", + " 18 1.144511e+00 5.373535e-02\n", + " * time: 0.03645205497741699\n", + " 19 1.144241e+00 4.641225e-02\n", + " * time: 0.03772401809692383\n", + " 20 1.144145e+00 4.074167e-02\n", + " * time: 0.03918814659118652\n", + " 21 1.144071e+00 1.511942e-02\n", + " * time: 0.04068303108215332\n", + " 22 1.144049e+00 8.172558e-03\n", + " * time: 0.042201995849609375\n", + " 23 1.144046e+00 6.918623e-03\n", + " * time: 0.0437312126159668\n", + " 24 1.144041e+00 4.671209e-03\n", + " * time: 0.045278072357177734\n", + " 25 1.144039e+00 6.508529e-03\n", + " * time: 0.046717166900634766\n", + " 26 1.144038e+00 4.824834e-03\n", + " * time: 0.048148155212402344\n", + " 27 1.144038e+00 3.575328e-03\n", + " * time: 0.04980802536010742\n", + " 28 1.144037e+00 1.041767e-03\n", + " * time: 0.051336050033569336\n", + " 29 1.144037e+00 5.773520e-04\n", + " * time: 0.053011178970336914\n", + " 30 1.144037e+00 4.450370e-04\n", + " * time: 0.05452108383178711\n", + " 31 1.144037e+00 3.969792e-04\n", + " * time: 0.055993080139160156\n", + " 32 1.144037e+00 3.824215e-04\n", + " * time: 0.05753517150878906\n", + " 33 1.144037e+00 2.719970e-04\n", + " * time: 0.05926108360290527\n", + " 34 1.144037e+00 1.627166e-04\n", + " * time: 0.0603640079498291\n", + " 35 1.144037e+00 1.507718e-04\n", + " * time: 0.06190299987792969\n", + " 36 1.144037e+00 7.870712e-05\n", + " * time: 0.06360101699829102\n", + " 37 1.144037e+00 7.800231e-05\n", + " * time: 0.06526803970336914\n", + " 38 1.144037e+00 6.424657e-05\n", + " * time: 0.06679010391235352\n", + " 39 1.144037e+00 5.827822e-05\n", + " * time: 0.06827211380004883\n", + " 40 1.144037e+00 3.554054e-05\n", + " * time: 0.0698080062866211\n", + " 41 1.144037e+00 1.921885e-05\n", + " * time: 0.07132101058959961\n", + " 42 1.144037e+00 1.213683e-05\n", + " * time: 0.07293200492858887\n", + " 43 1.144037e+00 2.846486e-06\n", + " * time: 0.07458209991455078\n", + " 44 1.144037e+00 2.602922e-06\n", + " * time: 0.07567310333251953\n", + " 45 1.144037e+00 1.464176e-06\n", + " * time: 0.07728409767150879\n", + " 46 1.144037e+00 1.034941e-06\n", + " * time: 0.07885909080505371\n", + " 47 1.144037e+00 6.158755e-07\n", + " * time: 0.0805811882019043\n", + " 48 1.144037e+00 3.931312e-07\n", + " * time: 0.08227920532226562\n", + " 49 1.144037e+00 2.027248e-07\n", + " * time: 0.08397603034973145\n", + " 50 1.144037e+00 1.286411e-07\n", + " * time: 0.08550310134887695\n", + " 51 1.144037e+00 1.286001e-07\n", + " * time: 0.08860301971435547\n" ] }, { @@ -322,7 +320,7 @@ { "output_type": "execute_result", "data": { - "text/plain": "8.846933145182664e-16" + "text/plain": "8.946221295479282e-16" }, "metadata": {}, "execution_count": 9 @@ -348,106 +346,106 @@ "output_type": "execute_result", "data": { "text/plain": "Plot{Plots.GRBackend() n=3}", - "image/png": 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", + "image/png": 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a/dev/examples/gross_pitaevskii/462e7a08.svg +++ b/dev/examples/gross_pitaevskii/4441faad.svg @@ -1,48 +1,48 @@ - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/examples/gross_pitaevskii/index.html b/dev/examples/gross_pitaevskii/index.html index 47324c7515..6bb1e6af83 100644 --- a/dev/examples/gross_pitaevskii/index.html +++ b/dev/examples/gross_pitaevskii/index.html @@ -21,17 +21,17 @@ ψ /= (ψ[div(end, 2)] / abs(ψ[div(end, 2)]));

    Check whether $ψ$ is normalised:

    x = a * vec(first.(DFTK.r_vectors(basis)))
     N = length(x)
     dx = a / N  # real-space grid spacing
    -@assert sum(abs2.(ψ)) * dx ≈ 1.0

    The density is simply built from ψ:

    norm(scfres.ρ - abs2.(ψ))
    8.986477946759718e-16

    We summarize the ground state in a nice plot:

    using Plots
    +@assert sum(abs2.(ψ)) * dx ≈ 1.0

    The density is simply built from ψ:

    norm(scfres.ρ - abs2.(ψ))
    8.068732333731283e-16

    We summarize the ground state in a nice plot:

    using Plots
     
     p = plot(x, real.(ψ), label="real(ψ)")
     plot!(p, x, imag.(ψ), label="imag(ψ)")
    -plot!(p, x, ρ, label="ρ")
    Example block output

    The energy_hamiltonian function can be used to get the energy and effective Hamiltonian (derivative of the energy with respect to the density matrix) of a particular state (ψ, occupation). The density ρ associated to this state is precomputed and passed to the routine as an optimization.

    E, ham = energy_hamiltonian(basis, scfres.ψ, scfres.occupation; ρ=scfres.ρ)
    +plot!(p, x, ρ, label="ρ")
    Example block output

    The energy_hamiltonian function can be used to get the energy and effective Hamiltonian (derivative of the energy with respect to the density matrix) of a particular state (ψ, occupation). The density ρ associated to this state is precomputed and passed to the routine as an optimization.

    E, ham = energy_hamiltonian(basis, scfres.ψ, scfres.occupation; ρ=scfres.ρ)
     @assert E.total == scfres.energies.total

    Now the Hamiltonian contains all the blocks corresponding to $k$-points. Here, we just have one $k$-point:

    H = ham.blocks[1];

    H can be used as a linear operator (efficiently using FFTs), or converted to a dense matrix:

    ψ11 = scfres.ψ[1][:, 1] # first k-point, first eigenvector
     Hmat = Array(H) # This is now just a plain Julia matrix,
     #                which we can compute and store in this simple 1D example
    -@assert norm(Hmat * ψ11 - H * ψ11) < 1e-10

    Let's check that ψ11 is indeed an eigenstate:

    norm(H * ψ11 - dot(ψ11, H * ψ11) * ψ11)
    1.1164732953263345e-7

    Build a finite-differences version of the GPE operator $H$, as a sanity check:

    A = Array(Tridiagonal(-ones(N - 1), 2ones(N), -ones(N - 1)))
    +@assert norm(Hmat * ψ11 - H * ψ11) < 1e-10

    Let's check that ψ11 is indeed an eigenstate:

    norm(H * ψ11 - dot(ψ11, H * ψ11) * ψ11)
    2.3886460349062613e-7

    Build a finite-differences version of the GPE operator $H$, as a sanity check:

    A = Array(Tridiagonal(-ones(N - 1), 2ones(N), -ones(N - 1)))
     A[1, end] = A[end, 1] = -1
     K = A / dx^2 / 2
     V = Diagonal(pot.(x) + C .* α .* (ρ.^(α-1)))
     H_findiff = K + V;
    -maximum(abs.(H_findiff*ψ - (dot(ψ, H_findiff*ψ) / dot(ψ, ψ)) * ψ))
    0.00022341476109513677
    +maximum(abs.(H_findiff*ψ - (dot(ψ, H_findiff*ψ) / dot(ψ, ψ)) * ψ))
    0.00022341807583437867
    diff --git a/dev/examples/gross_pitaevskii_2D.ipynb b/dev/examples/gross_pitaevskii_2D.ipynb index cfb611dd06..7207032c6f 100644 --- a/dev/examples/gross_pitaevskii_2D.ipynb +++ b/dev/examples/gross_pitaevskii_2D.ipynb @@ -25,1557 +25,1053 @@ "output_type": "stream", "text": [ "Iter Function value Gradient norm \n", - " 0 3.013474e+01 9.385289e+00\n", - " * time: 0.0025610923767089844\n", - " 1 2.959728e+01 4.798949e+00\n", - " * time: 0.06592512130737305\n", - " 2 2.185249e+01 5.523702e+00\n", - " * time: 0.07545113563537598\n", - " 3 1.631254e+01 5.419972e+00\n", - " * time: 0.08472204208374023\n", - " 4 1.232665e+01 2.607638e+00\n", - " * time: 0.09393906593322754\n", - " 5 1.032856e+01 1.061047e+00\n", - " * time: 0.1030130386352539\n", - " 6 9.698218e+00 1.408575e+00\n", - " * time: 0.11073899269104004\n", - " 7 9.229210e+00 1.025044e+00\n", - " * time: 0.11819195747375488\n", - " 8 8.840192e+00 9.872141e-01\n", - " * time: 0.12562203407287598\n", - " 9 8.568499e+00 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l3iA9E4TF5rRjQ8KowejovtxuMv/5Ix+b0uztpZN3arSiosLy27179zZp0mTUqFFCiAkTJlRXVycmJio5OgAAgAYmYyJ85513mjVrFhAQ8I9//ENqSU9Pj4iI+HNDTk5t27bNyMhQfIgAAGAv5FfllX8DvtLqOxE++eSThYWFpaWl33777dKlS7du3SqEKCkpcXX9310/bm5uxcXF5NNNJpPBQJ/XAgAAu1CfoEgrrhHaeh6s90TYtm1bac7r1avX1KlT9+zZI4Tw9/cvLCw09yksLGzevDn5dJVKhWuHAAB2TctWyfofK+ZBm19UtOb2icLCQnd3dyFETEzMf/7zn+rqaiFEWVnZ2bNnY2JiFB4gAABAQ6pv1Ogbb7zRv39/T0/PgwcPfvPNNz///LMQokePHhEREfPmzXviiSfef//93r17R0ZGNuRo70RK1NTl0omRlWy5LGjlVBSoEKK8hG4vK66u/0bIEFMhRAUVIMoV5mWzplHJutioUaZdVuo1NrJRDrYwr5xUakIINRUgqqHK2HKdhRCaSqJdViVkIURNNbFc4N417mCW9drKfRvIaFJZ+dhu0Ax/sv0CT7b6rghNJtOyZcvmzp2bnJx88ODBrl27Su3ffvttRUXFY489ptVqv/766wYbJwAA2AM7vEZY3xXhokWLyPagoKC1a9cqNx4AAIBGhVyjAACgGBV/2p99ir2sCAEAAG7ODq8RYiK0Me5qPxlJQEaFCJmlBNnglxIi+EUIUVYsK1iG3giXYo0sMUjWFxT87pNxMXpmI1wQDbdxLiUbjfv8y9kGHxRDt5PZ1DRM3UGNRka8FfeaGNiAI6KdewHlFteU9RpyKxK6XctEJyFWxirW1CO09WuKeoQAAODQsCIEAADFWJMyzdZLQqwIAQDAoWEiBAAAh4ZTowAAoBhrgmUaZiT1h4mwkbChcFw9WCowjwun5LKmkbGaZUzUaOl1LmqUaae2w6VS40ZIxrvqaxQI+DQwmb24jF9s/jYqEpKnQNioWi2vpq5RS+0+F2HLREiSoadyo0aZIsZkXzY6lE+xRoxcxUR28lGj9W0UQqiYdHRkOKmtL3LdRlRC9jVCuynDBAAAcEfCihAAAJRjxQ31tl5PYyIEAADlNEAS7aKioi+//LKoqOjee+/t1KlT3Q4mk2nbtm2///57x44dx4wZYz7Xmp6enpyc7OHhMWTIEHPnHTt2SNUDhRAtW7bs1asXTo0CAIBiVFa5wQbLysp69ux54MCB8vLyAQMG7N+/v26fZ555ZvHixUKIV1999cUXX5Qa//nPf951110vvvjiyy+/bNn50Ucf3bp16759+/bt25ecnCywIgQAgNvZF1984evr+/XXX6tUqmbNmi1dunTw4MGWHS5fvrx27dr09PTAwMC4uLgOHTosWLDAz8/vsccee/bZZzds2PDhhx/W2ubixYvDw8PN32IiVB4Z9cbFwsmqtVtFZeYUQlRS5W0FE8NZzuUUZQvwMgV7qbSi3EjInKKCiYPlAjg5zjoir6bOhU62yY6Qicgli9Nysb4y4h35iEedC92upXZTCOHiRnyKueOKq6mrp8J3NVouTJceCflLZecO5eoVUy+XmitizLy2ZDvXmXuDuO50q62vft0B9u3bN2rUKGnVOHr06Hnz5tXU1Gi1/6sCffDgwU6dOgUGBgohQkNDw8LCDh8+fN9997m5uXHb3LVrl6+vb7du3Tp06CAQNQoAAApS+syouHLlSkBAgPQ4ICDAaDTm5ORwHYQQgYGBWVlZN9hgZGTkb7/9lpCQ0KtXL+mEKlaEAABgY6dOnXriiScsWwYMGBAXFyeEcHJyMv73dlTpgVr9l3MSarXaaHG/qsFgqNWhlsOHD5t/abdu3R5++GFMhAAAoBgrMssIlfD29u7atatlW2RkpPQgKCgoOztbepydna1Wq/39/S17BgYGWq4Rc3JygoKC6vNrO3fu7Ovre+7cOUyEAACgKPlXRkNDQ2fOnEn+aMSIER999NFrr73m5OS0Y8eOYcOGaTQaIcQff/zh7e3t5+c3ZMiQRx99NDMzMyQkJDU1NTMzc8CAAdwvslwvnjlzJj8/Pzw8HBPhLeCu9lPRAVx4BZetiozd4FOpcfEsRDsXFFPOpFKTVWu3spzeOBcsQ8bFcNEVXr4uZHvLtl51Gz29dWTnq5dLyfb0lAKynaxjzIWcsIEh1H/Izs70FXqdD/2pJHdTCNG8pUfdRi5h3qU/rpPtxQWVdRvZdHRsYV4yWIbsy1IxcQtqMliGyYLGFjem2rmKx9xGVNQQVdypOJPDlfdVqWSnTLtx/8mTJ7///vujR48ODw/fuHHjrl27pPa4uLgpU6bMnTu3efPms2fPHj58+NixY7dt2/bSSy/5+PgIIZKTk5csWZKZmXn+/PlJkybdddddCxYs+PHHHxcuXNi9e/eKiort27e/8sorLVu2xEQIAAC3L1dX16SkpJ07dxYUFJw4caJ169ZS+/vvv28+Bbp8+fJ77rknJSUlPj5+4MCBUmNwcLDlKtPPz08Icffddzs7O6elpbm6uj7//PNRUVECwTIAAKAkK6pP3Ky/m5vbAw88UKuxd+/elt8OGTLEMn2MEMLX13fo0KG1nuXq6jp06NBa7ZgIAQBAOVbkGrU13EcIAAAODStCAABQjEqoVPZWfgITofXkBI3SNUsFH5hHhlnKjhqlsqlxKdbIEFPB5yQjB1NVISNRmWB2X8uEU3r70VGj7e9qVrfRvwURSymESD15jWzPySwh2w36srqNVZX0a2JicsORkZBOTlqiVQhXd/pTGdreh2yP7Ers/rUrxLCFEKXXq8j26/lE1Cj3rnEFe8ndZ8vesu0yYjjVTJ1hLhCULG7MVTxmQ1LpwrzM/jAn3ezt3KEM1lSot/XLgYkQAAAUZWcLQlwjBAAAx4YVIQAAKObmWbTrPqWBhlJvmAgBAEAx1uUatS2cGgUAAIeGFeGtYGLnqLBRMg2jYCrTCiZqlC3AS6X95Nq5jVQyIalcqCo5Qm532HqwcmrwcglLyRFWMpG0XDZULhCUjJysocrYCj6jLFniVa1mdkfObgomUpethFxFj5x849iUqtxuUv/Xs2VvuahRpj8Z26nV0ik+NUy71ploJxuFEBqmnYwmdVIzrwnZeuOf2Dsrbqi39YuBiRAAAJRjzTVC3EcIAAB3CjtcEOIaIQAAODasCAEAQDFW3D5h89QymAhvjqssyrWT2dTYVGpM8AIZusKlUuMDQ8gsaFzwi4ygGMFGkTAxGmw7sftc5yvpxWT7CepD5OHlTHa+epnOPZafU062ky+XnguWYY4J8u9ClYp+wQuYkfx2LJdsJ0dexiTSy0qnC/OWUWWZ+SNfRrAMF/zCpiRjyuFWkcEyTDwLl6WPbHfWMRth2jXURrikbtzuc2w9IyjE3s6N4tQoAAA4NKwIAQBAMdYk3W6YkdQfJkIAAFCMSsXX4uCfYluYCAEAQDl2eP8ErhECAIBDw4qwPrgypHQ7mU2NixrlslhV02VvmexocrKm8VGgXFyrjEBQLvcY106GqnJ1X7ndJ4vKOjFRfNxrRYZNcv25d9PIHBJkmjGDgf43tDCvgmyvSabfiEvniUBQg54eIfcakgetmgng5P5ukBGSKid62GzUKFMOV6ulSlU707vDRY06uxAj57Lr6aro3dRT0aRcdV8nJybClo5IFbZfHN0yO1wQYiIEAAAF2eF9hDg1CgAADg0rQgAAUJTNz3XKJG9FWFNTExsb++CDD5pbUlJSBg8eHBgYOGLEiAsXLig9PAAAsCfSfYRyv2xL3orwrbfeunjxopPTn9OnyWS6//77H3rooa1bt77zzjuTJ08+duxYAwyyschMpWZiSukxKdZkJCoTTFE6svKc4K/211QR7VwCMz3XzgSGkGnG+M4yUqxxGzFV0O9EWQmRYY58FwQfRSIrCRy3O7KCZbhyd9wIuSgnjbaqbiOb2IxsZfrzASD0SMjsaE7MHzmuTiF/qBCDqaaOcCFEVSX92pKflKpK+s8g99nUUZ9lo57+jSYmKMbErEHkvW23JStyjcq+pqg0GSvCM2fO7Ny587nnnjO3JCYm5ufnL1iwwMfH5/XXX09NTT158mQDDBIAAKCh1Hci1Ov1jz/++IcffqjVas2NKSkpMTExarVaCKHT6SIjI3/77bcGGSYAANgFlfwvW6vvRPjOO+/07t27T58+lo35+fmenp7mb729vfPy8sinm0wmvZ4+iQEAAHahpoYudGPJmguEtp4L6zURZmRkfPTRR4888siFCxdyc3OrqqouXLhgMpl8fHxKS0vN3a5fv+7r60tuQaVSaTSIUAUAsGOWZwTvJPWanPLy8gICAmbMmCGEKCgouHr16qRJkw4fPty2bduUlBSTyaRSqfR6/blz59q0adPAAwYAgNuXtMyT+RQbq9dE2K1bt+PHj0uPN23atGLFCunbwYMHq1Sqzz77bMaMGR9//HGzZs169+7dgINtYExwKPsDIxNOajDIiITkghXJiDU+2xkTq1lDjJBMpiX4iEeuCit58HIHNLcN8rXiwia5kZOvLRemy2ZHY/K6kb+UHLbgd1OlIn5gZKKODTJrOGu0xKuu1dLRimomEJTMpqZiThgZuVBIObgMhdxxyGQupDuzHysq8pYLx2Xjq6k3iDskjEx4qJOJ/qyQzTafJ+SxwxxrsjPLeHh4BAcHS481Gs3XX3+9YsUKd3f3Tz/99Msvv7R5FCwAANxhduzY0bNnz/bt2y9YsIAMNzl//vzYsWPbtGkzfvz4ixcvSo0pKSlvvPHG2LFjZ82aZe555cqVOXPmdOvWLSoq6pFHHrl8+bKwYiIcM2bMrl27zN/27t07JSWltLQ0OTm5S5cusvcPAADuKCrZbrgkTEtLmzp16uuvv75r1679+/evWLGibp/7778/Kirq0KFDISEhkydPNj+xuLjYz8/v6NGj5p6ZmZmenp4rV67ctm1bVVXVuHHjhFIp1rAQBAAAYVWF+hufGo2Pjx87duzo0aOFEAsXLnz66adffvllyw5JSUmXL19etGiRRqN56623mjVrdurUqc6dO48dO3bs2LEbNmw4c+aMuXPv3r3Nl/CWLl0aFhZWXFyMpNsAAKAcK+4jvOFE+Ntvv3Xt2lV63LVr18zMTMu7FYQQZ8+ejY6Olm5McHFx6dix49mzZ+sz0iNHjrRq1apJkyaYCAEA4PaVm5vr5eUlPfb29pZaLDtcu3bN3EHqU6sDKTMzc+7cuR9++KFA9Yn64KJJ2bA3Os5QRsSj4MreMlGjsurEckk4OS5u9J1Dnt5U7kcmAK/wGl1slsz9yL2w3G6S1X25kXC7L+tF4a4FyDojxIVHcu0qNo8r8VuNLvRGnJmPvBMVIcqlA3VxpTfi7etC/EYXOsSUezc55BtnZGI1ZdXB5rKbcuG75GeZPa64YGyy1fbhkwqwJteoUH377be1njVnzpz3339fCGF5w3pJSYnUYtnT29u7rKzM/G1xcXGtDnVlZWUNGTLk1VdfHTNmjMBECAAACrLu7olx48Zt376d/GlYWNjvv/8uPf7999+bNm1aa54LCwtLTU2V7mg3Go03vaM9Jydn6NChM2fOfPbZZ6UWnBoFAIDb17Rp0zZv3pyZmWkwGN57772pU6dK7e+9996hQ4eEEEOHDjUajV999ZUQYt26dZ6enn379hVC1NTUFBYWlpeX6/X6wsJCaVl57dq1IUOGjB49+vHHHy8sLCwsLDQYDFgRAgCAcpS+ob5Xr17z5s2LiYlxcnLq2bPnwoULpfaEhAQPD4+BAwdqNJovvvhi+vTpzz77rJeX1xdffCHVCvz555/Hjx8vdW7Tps0999zz5Zdf/vLLL9nZ2WvXrl27dq30o6NHj2IiBAAAxaisuKHuZt1feeWV+fPnV1dXu7q6mht3795tfjxw4MCMjIzS0lIPDw9z4913311QUFBrU+PGjZPuHbSEibAemMRZsgYkq04AACAASURBVHJBsWnDmHY6bRjTmbtQT8cXMMPmqrD6B3uQ7UGtPes2cqWD007TZUkqK6hCqVSjuFFNXRmvFYf96FLvvrxwI+438kMhm7mUfmRyOBUVQSOEUGuYl8WZePd1LvTfh4BW9CHRthORc1/HRNZkZ5aQ7XnZZWQ7Hfklpzi2YI4Ktpq0nM+s3MyFbDo++w+XseI+wvp0V6vVlrMgyXIWlAXXCAEAwKFhRQgAAMqxw6TbmAgBAEA5VtxHaOsknTg1CgAADg0rQgAAUIziSbcbASZCS1xlWqY3V7BXTmIzA1MPlgwFZDvLSdbF5XziovuCWjch22P6BNRtrCynAz5Lr1eT7TlU6GAx87Fgs1VxgXkUJ+Yzym2C3DaTekw4qelTLGQmMG7U3Ma500dk5WTZrxW1cRc3+pAIDqMPic79Aus2cscVl7+tuKCSbCdz5slNU0d+griPFftxkxOMLfevB3Mk2nqiuNNhIgQAAMWohDW5RhtoMPWEa4QAAODQsCIEAADFWHNDva1P/WIiBAAAxVhThsnWMyFOjQIAgEPDitCC3AySXDwYnRRRiYBPLtskm2uUClaUORJ9DZ0+lMwIyqUJ5TbCJJCUN0LyVZEVYyn4rJU6V6KurH8LOqVhEx8d2V5cUFW3MTerlOzM5Wt1Yv5rJfdUVkizYA5abiOyqkxrmOymcsMsmWBsedl3yXdfmc8mm1OUbmbdAUGjVmSWsTVMhAAAoBzcRwgAAI5MJVRyb4ew9TyIa4QAAODYsCIEAADloPqE3VCisqpJzlZMTCwGF7vB/UpZG2FKBNOdy0tqyPbM1CKyvayY6M8Fy3AbIX8pW8RYTgQEh00xxpwcIeNi+t8bSnZu25GoTCuEOHeGqEt8eFcG2TnrfDHZLislm9y4KrIIbXkJnRjvYhr9bqrVxFC4FGu5WXQB3vJS+jgkD35ZQTHcRjjcRmR+ZJX5W0Oz9fxBUsm/L9Dm+4FTowAA4NAcdUUIAAANQKWSf4O8rW+ox0QIAADKwTVCAABwZNbkGm2YkdQfrhECAIBDc9QVIfkfiMzwLmVqaMmJB+MiG+Ule2OyUlVX0+3ZVO1cIUTORSJDGBc1WnqdyDEmmEK+XNSokWlnUqyRfdl2JyriUTBZ09pENSU7R1PFioUQBuo1P/VztqyRcBm/yD1iA4nlFKGtYMosZ2fQh0RpERFlSuaoEzdKYEY2C2cdcfRzG+E+EfQniPscc59NOX8olPkrYfMVkxxIug0AAGBnMBECAIBDc9RTowAA0DCQdBsAAByXNdcIG2go9YaJ8BbICcfgYzfoH5DtKjKbFj8SEhsBwMQdVFUy9QgrZdQjrGEiccgoEi4dHRdGQY5cxYSccC84l6yrKK+ybmPqKSJlmmAK8gkh/vg1v25jcT6xZXGjoBgZbzP/GsqoR8hFLXFZ0Mjd54JlnF3oPz7OOrq/rMRm7GeT+gTJ+gwKmR9w2/+Nb3x2eB8hrhECAIBDw4oQAAAUY4+3T2AiBAAAxViRWcbmcGoUAABud7/99ltSUlJVFZ2aQwiRk5OTmJiYm5tbqz0vLy8rK6tuY2Ji4pUrV6RvMRECAMDty2g0Tpw48d57712wYEFERMT58+fr9omPj+/YseOSJUs6dOjwxRdfSI1bt24NCQnx9/e/7777LDtv3bq1ffv2S5YsiYmJ+eijjwROjf6FzKRcbDsVmcblzWLbyY3IDG8jh0humekrBF9ZlAyz5GrnykqFxWWSc2LaDXSOMboz9wMuavTaFaJ+7M/fZ5CdT/9CZ00rKSL+jc2jtnyDkajpaEp54ZTca0i/5syWZVX31ejpX8klgZMXXy3zE0F+gmR/Nql2LqJb7l8Pm8dP3jrFrxHu3r375MmTycnJHh4e8+bNW7hw4YYNGyw7lJaWPv/883v27OnVq9eBAwcmTZo0YcIEnU4XExOzc+fOkydPfvLJJ+bOer1+zpw5n332WWxsbHJycp8+feLi4rAiBACA29eWLVsmTJjg4eEhhJg+ffrWrVuNf72Vau/evYGBgb169RJC3H333e7u7gcOHBBCtG3bNiYmRqP5y3rv3//+d3V19ejRo4UQ0dHRkZGRu3btwkQIAABKkuJlZH3dwMWLF0NDQ6XHrVu3rqioyMvL4zoIIUJDQy9dunSDrbVq1crpv+dGpM44NQoAAMqx6ob6jIyM1atXW7Z16dKle/fuQoiKigqd7s8iMNKD8vJyy56VlZXOzs7mb3U6Xa0ON+1c34lw79693377bU5Ojp+f37Rp0/r16ye1X79+fdmyZampqZ06dXrppZfc3d3ruUEAALjzWHGNUAhRVFR04sQJyxYPDw9pIgwICMjP/zM3U15enkqlCgj4S72z5s2bmzsIIfLz8wMDA7lfVKtzXl5e79696zsRXrhwITo6euTIkampqSNGjPjhhx/69+8vhJg4cWKTJk1mzpz50UcfTZ8+fcuWLfXcIAAAgKRz586rVq0if9SzZ8/ExMQXX3xRCHH48OHo6GgXFxfLDj169Jg1a1ZZWZm7u3tRUdGvv/7arVs37hfdddddGRkZOTk5AQEB1dXV//d//7d48eL6ToRPPvmk9CA2NvbYsWMJCQn9+/c/c+ZMUlJSbm6um5tbz549AwMDL1y4EBYWVs9t3n7kFehkI9PIgE8mqIyLTFPLikyTE2bJRo1yI5TTn49rJZvpjag19P5ouSyUVKORqUBr4t41JiazpopItZojp1ixEHRkJxftyR0Ssu5S1mjpztxrSL7mso83OQlyZR+H1MsiLwiWO95kfjZlfcDlr43sPmzUihvqb9z/kUceWbFixdKlS9u1azd//vwlS5ZI7UOHDh0/fvxTTz0VFRU1YMCAhx566NFHH/3kk0/uvfdeaRpKT0/fvHnzf/7zn+zs7LfffrtNmzYTJkwICgqaPHnyQw89NHfu3I0bN0ZHR3fv3l1esIzBYEhOTj569OiAAQOEEMeOHevataubm5sQwsfHp0OHDsePH5f3AgAAwJ1EZdUXr3nz5ocPH7506dK2bdveeeedadOmSe33339/dHS09Hjz5s0xMTGff/557969P//8c6lRr9cXFhaGhobGxcUVFhaWlv75f+rq1auHDh36+eeft23bdseOHULWfYSrVq164YUXSktL58yZM2TIECFETk5O06ZNzR38/PxycnLI55pMJoOBLmIAAAB2Qa/X17oboXF06NBh5cqVtRqffvpp82NPT8+FCxfW6hAeHr5s2bK6W3NxcVmwYIFli4wV4RNPPFFSUnLhwoWDBw++++67Qgh3d3fLhDcVFRVcsIxKpXLizmIAAIA9qM8sqBJC/t0TNj4hLHtyat269UMPPbRv3z4hRIsWLTIyMsw/yszMbNmyJfdEm+cXBwCAhqay4j5CW4+5vovcS5cuSZNcVVXV3r17O3XqJIS45557ZsyYceTIkV69eu3bt6+ysnLgwIENOFgbkVWfU8hMscYFhqg11FV9NrKG3ogT1S43eIFr11AjN7rQsRhO1O4IpvQr9xtlqRb0qXgDE0TDvsvUS8uWt6XqDAuZh4S8CrTMocKVt9W50h95HVUmV+vMHZwyDloN89bLPd7oEBXmyOc+EeQniBy24HeTCduRm2LN5n/8G4x1hXllHvbKqu9EOGjQIBcXF19f399//z06Ovq1114TQnh4eLz33nv33ntvTEzM6dOn//Wvf5lvewQAALAL9Z0I09LS0tLSioqKWrZs2aJFC3P7jBkz7r333vPnz0dERPj6+jbMIAEAwE5YcUO9SmUfK0K1Wh0ZGUn+yN/f39/fX7khAQCAvbLmPsKGGUn9IZITAAAcGpJuAwCAYqyqR9hAY6kvTIT1ICc6VDDxY1wEGhl7KYRQa4l2slHcIOxNTnib3N3U6ojBuLgxQYmuTAYzSjFVxlYIcT2vkmw3UTGcXPFYg56OJuX6k1GjbIgplx2N/I1c6CkdecpGmZKHkDMVBSqEcPPQku1evi51Gz28uNg3euRVlcRry7+w8o5DcvfZOGruEyHnY8V+NuWlo5O3m2ATmAgBAEAxVlwjtPm/BbhGCAAADg0rQgAAUIxKyL9GaOslISZCAABQjnWZZWwKp0YBAMChYUX4P+xqnkl5wC3/yUg2LgJNw6Rz1GqJMEstE96mdaZjMjVUf7JR3CA9o4aOYtQ6EwePbwBdfiS0nTfZTkaZZqQWkZ3TTuWR7TXVxAgNXNQo015jklEmjE8gSbeT6UO56FCuqCz3xpG1dnVM0lefZq5ke0Rnv7qNLdt6kZ3LS2vI9kzqjSvMrSA7m5icqtxxSH6C2NeE+USQnyDysyb4zyY5Ei5+lQ0wJltvgxsJbp3ihXkbASZCAABQjhUp1mx9bhQTIQAAKAfXCAEAAOwLVoQAAKAYO1wQYiKsDy7nE7OcpnNBcWmcuHbqQj0ZFnGDdrI6a00VHRXizIRXcMFCGirEwL8FHSzTsWdzsp3M7MW9VjkXS8j26/lE6jWy5O8NcNFMZCI0Lp6FuzRCJoHjCvCylZCZ2A3yXeYK83r7ES+4ECI8mgiW6dSLftfyr5aT7RVUEA3ZKITQM28QdxySVYK53ZT1SeHqD3OxaeTxySXA4/5K2P5vf4NRyS87bPNgGZwaBQAAh4YVIQAAKMcOz41iIgQAAMVYk3Tb1jARAgCAYqyqR2jjmRPXCAEAwKFhRVgfMiuI0oV5mfK2TGQaHQrIBNRxsXMaKtEUF1DH5R4jIx4F80+coYYOBawoo0MHyRFWU/VdhWBz3ckK09Ux/3tqnemRk4nQ2P9fuahROsearG0IJzYHHlV+mQliZH6nqKkmXvPKcj3ZuaqCKW5MHSrcSJzV8g5mJuBT3kbITxD3ieDCdGW94Dc4Vph2+4drhAAA4NhUcssq2bwME06NAgCAQ8OKEAAAFKO6DW6QlwsTIQAAKAfXCO9IciuHkZfNuQxesq7261zo98tZRwc1kLmjuN9oZIJlyAgIwaRqy8uhk28l/zuHGSExmKuXSsnOZUyyLpITVweOOeTVQkZNR7KMomDSzgmZoShyk8ORUUtc/ray4mqy/fyv+XUbS4uqyM6VTLBMwVW69CBJVl5AwRwqstLOCeYTxP5Grk4h9VnmEuOxpSvp5jsBbp8AAACwM1gRAgCAYqypUH+zDhcuXFi5cuX169fHjRs3cuTIuh0qKir+9a9/paamRkVFPf30087OzlL7yZMn161bZzQap02b1qNHDyFEVlbWxo0bLZ87btw4rAgBAEA5Kqu+ePn5+b179xZC9OzZ85FHHtmyZUvdPg899NDevXvvvvvuHTt2PProo1Lj2bNnBw0a1KpVq/Dw8GHDhp04cUIIodfrC/8rNTX15ZdfVqvVWBECAMDta926dZ07d16+fLkQQq1Wv/322xMmTLDskJaWtnv37pycHC8vr+HDh7do0WLp0qWtWrX64IMPHn744RdeeEEIcfXq1ffee2/jxo0hISHLli2Tnrhs2bKBAwe2bdsWK0IAAFCMSr4brwiTkpIGDx4sPb777rtPnDhRVfWXMK5///vfXbp08fLyEkL4+fl16NDh//7v/+o+MSkpqdaW169fP2PGDIFrhPUiM+6LSbEmowCvYHJBsSnWmHYyRk5fTQclctGhbDsVZcpVYc1MK6r/RirL6HBKPVNSmEmxxtXIZcreMinZvKnSwS3aeJGdyTrDQoiCa0Q4Zdb562Tn6wVEnWEhhEHP5LqjBq5mMn5x6euyM4iix4XX6JFwEZJkJjnuyJdbU1dWHLXOlftEyPhY8SnWFIgavYPDRhW/eyI7O9vP78/C0c2aNTOZTDk5OSEhIeYOOTk55g5Sn+zs7Frt5kazw4cPZ2dnjx8/XmAiBAAAm/vll1+GDRtm2TJ69Oi5c+cKIXQ6XXX1n7f9SA9cXP7yH6dOp6up+d+/4FVVVVKHWk+s9az4+PgpU6a4ubkJTIQAAKAw+evd8PDw+fPnW7ZERERID1q0aHH58mXp8aVLl5ydnZs1a2bZMzg4+NKlS+ZvL126FBwcLLVbPlFqlJSWlm7dunX//v3St7hGCAAAyrHiIqFK5e/vP/SvWrVqJW3vvvvu27p1a2VlpRBi06ZNY8eOdXJyEkIcOnTo/PnzQojhw4dnZmaePn1aCHHkyJGCgoK7775beuKmTZukM/abNm267777zGPctGlTaGho9+7dpW+xIgQAAMVYcx/hDfuPGTNmzZo1PXr0CAkJOX78+E8//SS1v/TSS1OmTJk7d66Xl9fSpUvvueee/v37JyYmLl++XDrh+dRTT3311Vf9+/fXarU5OTkrV640b3Pt2rXmuywEJkIAALidaTSa77///tixYwUFBX379m3SpInUvnnzZvPjZ599dtSoUampqcuXL2/durXU6O3tfezYsaSkJKPR2LdvX51OZ97mpk2bgoKC/vcrGmtf7kgy4sS4up1srlEqko1LcVldqaXbqTBLfQ0dN2gwMNGkBiY3KRVNWlVBRzZypVyZEdIjcWKKG5OhgFzYJJeEU+dKv7aBrZvUbbxrUFDdRiFEizA6mjTj98K6jVwRY273yddKMMcbFwTLHYc1VCyxyUTHAGvZKGUZMZnckU8myBVMIKirO/2uubjRnwgd9QlyZkJPuRGSryGfLfPODQ/lWBE2ejNOTk49e/as1WgZOCqEaNu2bdu2bWv1cXZ2HjRoUN0NhoWFWX6LiRAAABSjUslPoo2k2wAAADaEFSEAACjGisK8tl4QYiIEAADlqMTNcqYRT7ExTITWk3V1nMvAxAU1OOuIt8bFlQ6jqPFg2ql6sFyMhkFmYV6TiQglMMnM00b253PXce3EM7TO8kbCJety9yDCLrya0qnUvP3o9iZUfzdqy4IJ6BB8nAu5+1xnrkA0mTaMOzi5eBYyCxqfSo0LiqF339WdeLncPOnX0JV5bV2ojXMjlBVwxBbgtfnf+MZnhxXqcY0QAAAcGlaEAACgHPk31NscJkIAAFDMn5WVZD6Fub+3kdR3IiwoKEhISLh48WKrVq3Gjh1rTuNtMpm+++67lJSUmJiYESNGNNg4AQAAGkR9rxF27dr1yy+/zM/P//jjjzt37lxU9Gd5udmzZ7/66qvl5eVz58595ZVXGmycAABgD1RWfdlUfVeEx44dkyocGgyGjh07btu2bcaMGVlZWfHx8X/88UdwcPDUqVOjo6Off/55X1/fhhywPaBOC3ARj2ombRgZU6cz0O+XQc8Eguqd6zbyUaD0CDmyk0fUeyNqKthVCKHXcxGPxO7zr4mM8rZCiNLi6rqNF34rIDsX5dGVbK9dKavbWM4UMeYCjLmMX+QhxJXDVauZAtFUHWMN8xvJVGqCCfjkokC5fIHunsRBK4Rw9yLa3ZjOblSIqeBSrDHxq9xn04k88u3usliDsSbpthC2PTVa3xWhuc6vWq12dnZWq9VCiAMHDnTq1Ekq8tS2bdvQ0NDDhw830EABAOD2p/pzKpTxZfMloezbJzZv3pyXlzd27FghxJUrVwIDA80/CggIuHLlCvksk8lkNNL/oQMAgF24U/+My4saPXTo0KxZs3bs2OHt7S2kUB+LE2omk4k7XabIaTQAALAD9nZDvYyJMCkpadKkSV999VXv3r2llsDAwOzsbHOHnJwcywpPtThxV8kAAMAe1OfPuOKFeRtBfSenkydP3n///WvXrh08eLC5cfDgwb/++uvly5eFEGlpaRcvXhwwYECDDBMAAKBh1HdFOGrUKHd39/Xr169fv14Icd99902ZMiUoKOiJJ54YPnz4fffdt3nz5ueff97Hx6chR2sf6P9uuDyZJi4HKZm2kcmfycTIkQGiJiY8lM2cygQxkpVvuXK4XOpLMoixmol4rGFKCutriI0bqEYhhEEtLzytjIoaPf8rHTV60fk62V5dpa/byEWNcgGfGubDSkeNMnkyuVyjZEgql4TTmaqRK5hMnlx0qKsHHfDp0YQ+mN29dEQjEzXqwhTsJfdITX3WBB++SwYY23xNc/uw5ob6BhpKvdV3Ivz4448Nhv/9GYqMjJQevP/++z/++OPZs2dXrVpluVgEAABHZIdJt+s7Ed5///3cj+6555577rlHofEAAIA9syLXqK0nQgSwAACAQ0PSbQAAUIxK/v1ycgv5Kg4TYaORceFdCKGmrt9zSbZMzE2uZNlbPpWavHtA6WAZOXVfhRAaDRFFwgZ6VNPt+mpi//VM/WGjgW6XFUJTWU4M+wbtJC6Dl6ua/lRyf1ucqIAjrqishqmpSx5aslKpCSYuhiyoK/iaulz8C9nflQmK4UZI7iYX4cWFidn+RB4oDadGAQDAoWFFCAAAirGuHmEDDaaeMBECAIBirMgsY/OTzZgIAQBAObYvJiEbrhECAIBDw4qwkfDnCricZESjhvvHhQl5NMmJheRO08sqKcwFK2rZYEWiXetMp1Kr5gr2Uu1c1KjBwNQlVqhe8a3jDhVZue74qFEmaxqVe4yLGnVxowM+mahRLsUaE03KpF4jt0MW2hVCODO7Sb4sXP4/vpYO2Qx/suoaYQONpb4wEQIAgHLu4OoTAAAAdyRMhAAA4NBwahQAABSjElaUYcJ9hI5NVhANF7fCRUYIFfH+siEATCwGF0pAVkzUOtM19rRMZTutjshJ5uxCJyqrrqKDZWqodjZYRi+v3cikr6Nx7yYdcsNFJzFBMUxKNjJ9HRu1xLwRZFyMs4uMVGpCCFeq3YVJscZthGsns6ZxFRO5sovka4ugGGU1xH2EH3300aJFiyoqKkaNGhUfH+/h4VGrw9GjRx999NFz585FRkauW7cuJiZGCGEymebPn7969WohxPTp09977z0nJychRNeuXYuKiqQnjho16sMPP8SpUQAAuH2dPHny9ddfP3jwYG5ubnFx8eLFi2t1MBgMkydPnjVrVnl5+fTp06dMmSKVH//666+3b9+empp6/vz5n376ad26dVL/zMzMNWvWJCQkJCQk/O1vfxO4RggAAEpSWfXFW7du3cSJEzt06ODq6jp//nzzfGZ24MCBqqqqmTNnOjk5zZo1Kzc398iRI0KIzz777KmnnmrevLmvr+/s2bM/++wz81NatmwZFhYWFhbWvHlzgYkQAAAUpLLKDTaYlpbWsWNH6XHHjh1zc3PNJzYl586di4qKkjai0WjatWuXlpYmtVs+8dy5c+anDBs2LCQkZNKkSRcuXBC4RggAADZXVlYmzUlmfn5+TZo0EUIUFhaaLwp6enoKIQoKCry9vc09CwsL3d3dzd82adKkoKCg7hOlRiHE+vXru3TpUllZuWTJkhEjRpw5cwYTIQAAKMaKYBmVEElJScOGDbNsjIuLW7RokRDCz8/v+vXrUqO0FmzWrJllT19f3+LiYvO3RUVFUgdfX1/LJ5qfNXr0aOnB6tWrfXx8Tp8+jYnwNkUfSVxgJ3PYaaiyv2x9VzZYkUnWRcUlcqnUnNkSr0R7VQV9WFZXMtGklUTUaA1VrVcIoeeiRuVEmTLp2OTh3jW2uDGXNU3WG8FGjVIxmVwBXupdE0xgpwuzEW7j3AjJPeJeK1lZ0xAdqjz5UaPDhg3bvn07+cP27dsnJydLj5OTk4OCgqR1oWWHX3/91WAwqNXq6urqlJSU9u3bS+2nT58eOXKk9MR27dqR2zeZTLhGCAAAt68ZM2Zs3br1559/zsvLW7JkyeOPPy61v/TSS999950QYsCAAU2bNl2xYkVJSclbb73VunXrbt26CSEee+yxjz/+ODU1NT09/YMPPnjssceEEL///vv333+fk5OTkZHx5JNPBgQExMTEYEUIAABKUvYG+aioqE8++WTmzJlFRUX33Xffyy+/LLUXFBSUl5cLIVQq1bZt25555pl33303JiZm8+bNUodx48alpqaOHDnSYDA89thjU6ZMEULU1NQsW7bs/PnzOp2uV69ee/bscXFxUZkaJbt+RUXFM4uW3/vwzEb4XXcw7r3i3kQjVWmBu3OcO5fI3cZeVUGcqCQbb9BeSW+EqT6BU6MUnBqtC6dGG8j97QJv2id+T1puUaWszf52fH/p+UPcqdFGgBUhAAAoxpoyTLZOsYZrhAAA4NCwIrQnilT3VVGhpEJm3VchhIZKfanl6r6yUaPEEVhFneoUSp0alZmDlDyVSp5zFvw5avIfZO6FZU+NNmj4LnlqlOnMtuvIdKBcTWZ6I9wZYPLl4g5apA+1pZtliqGfYlOYCAEAQDENkXS7oeHUKAAAODSsCAEAQDH2WI8QK0IAAHBoWBHeCWQF0ajoGAU2voBLvUbeqqXW0iEn/G1tRH+dK1eAl0m9Jqcwr+yCvVRcjOxgGeo1VMushCwrWEbDveBMiApZsJeLZ+HeTfKXcjVy2VsAufgXJsaLbrX1NSdHZk2uUVu/X1gRAgCAQ8OKEAAAlCP/hnpbXyLERAgAAAqy4j5CW8OpUQAAcGhYEQIAgGLsMVgGE+GdjMszRfdmzg5wRRLIVG1cxCMXOqh1JsIs9TV0sKLehY4mJQNB+ehQOrDTYKD7m6hKE2z1Ca4+CPVOsC8sm+uOq0pB9OdecA0TCEr2J+NRhRBqKrueYN59PgoU2dHuTNYk3bb1u4uJEAAAFGVv/7XgGiEAADg0rAgBAEAxdhg0iokQAACUY4/XCHFqFAAAHBpWhPAnuVV/yZylXCigkxMdTqmmNsKVZtUa6HYyENTIRIGSuUOFEEYmEtREbYbLKSoLGx7Jhe/KqZzsxIWYMgGfZEiqzLSf9LvPHVdswQG7O60GtaAwLwAAODSV7FOdNj81iokQAAAUY80N9Q0zkvqTd40wJycnJyenVmN2dvbBgwevXr2q3KgAAAAaSX0nwtWrVwcGBgYFBT322GO12jt16rRs2bKoqKhNmzY1wAgBAMCuqOR/2VR9T43269dv//79u3btOnTokLmxpKTkhRde2LdvX48ehILfRAAAD+9JREFUPfbv3z958uTx48frdLqGGSrYhtwgGrork3pNUEE0Tia6s5HL36YlNmIy0rnE2KAYJv7l9gmWkVU5WVY8ixDCido4+9bLOfNl66s/0NhUQsVGQt2u6rsi7NChQ2RkpJPTX/r/+OOPLVu27NGjhxBi8ODBbm5uBw8eVHyIAAAADeeWgmUuXboUGhpq/jYkJOTixYtcZ0X+iQYAgNuZw1WfqKysdHZ2Nn+r0+kqKirIniaTyWik7+sCAAC7oNfrNZqbzRq3wTU/uW5pIgwICMjPzzd/m5eXFxgYSPZUqVRq8t5pAACwEzefBR0wxVqPHj1OnDhRVlYmhCgoKEhJSenevbtCAwMAAGgM9V0Rnj17dteuXYcPHz5//vzbb78dHR09cuTIqKioAQMGxMXFzZgx45NPPhkzZozlJUNwQLL/sSOfwFxNZrKGCRMZZcpshL1SzUWNUs389W7uB3Jyj8mM1ZRVfVlWDLCt/00Hu2TFNUKbn0qt74qwurq6sLCwY8eOY8eOLSwslFaBQojNmzd36dJl48aNAwYMWL9+fYONEwAA7IG93UQo6r8i7NKlS5cuXeq2e3p6vvHGG4oOCQAA4H+qq6sTEhKKioqGDBkSEBBA9jl27FhKSkqnTp0sp6qSkpK9e/cajcbhw4d7eXlJjdeuXUtKSiovL+/WrVt4eLhAGSYAAFCUygo32Fx1dfWgQYP+/ve/7927Nyoq6uTJk3X7LFq0aMKECb/88ktsbOw777wjNebm5sbExKxbt+6rr77q1KnTlStXhBCHDx8ODw//9NNPv//+++7du7/77rsCSbcBAEBBildh2rJlS2lp6YkTJ7Ra7eLFi998880dO3ZYdsjPz1+2bNmpU6ciIiKSk5P79es3c+bMJk2afPzxxzExMdu3bxdCTJ069f3331++fHn79u0zMjK8vb2FEAkJCWPGjJk9ezZWhHDbkS62U19yONFfTtyXmv5Sk18a7suJ+aI6MxvnRsKNnN5T+f+R1/0CsIYV1whveLDt2rVr3LhxWq1WCDFhwoTdu3cbDAbLDgkJCREREREREUKI6OjowMBAKcfZd999N2HCBKnP+PHjv/vuOyFEs2bNpFlQCBEUFKTX6/V6PSZCAAC4fWVlZQUHB0uPW7RoodfraxU7suwghAgODs7Kyqr7xMuXL9fa8uLFix988EFXV1ecGgUAAMXc9Jof6ezZswsWLLBsGTBgwKhRo4QQer3enI9FeqDX6y17GgwGyzzYGo1G6mAwGCyfWGsduXjx4rNnzyYmJgpcIwQAAAVZl2tUq9X6+PhYNprzdwYFBeXm5kqPr169qlKpagWOBgYGXrt2zfzt1atXg4KCpHbLJ0qNkhUrVnzxxRcHDx6UfikmQgAAsLGIiIj58+eTPxo4cOCWLVteeeUVIURCQkLfvn2lObK4uFin0+l0uv79+z/++ON5eXl+fn5XrlxJS0vr27evEGLQoEF79+4dP3689MRBgwZJG/zggw8+/vjjgwcPmidUTIQAAKAYKa5N5lNu1H/q1KkrVqx4/PHH27Vr9/e//33Dhg1S+7Bhw6ZMmTJ37tzQ0NDJkyfHxsbGxcWtX7/+4Ycflma42bNn9+jRw9fX19nZOT4+/pdffhFCHDhwYN68eSNGjHjrrbek7SxatAgTIdg9heIbuaRk9e8KAEIIhT8hXl5eR48eXbduXWFh4ffff9+rVy+p/eWXX27Tpo30OD4+fuPGjSkpKXPmzHnwwQelxvDw8GPHjm3atMlgMBw5cqR9+/ZCiJCQkJUrV1puX6fTqRqnTGBFRcUzi5bf+/DMRvhdAADQEO5vR9cXsrTtxKX8smpZmz11OOHK8R+lG/5sAitCAABQkt2dMsFECAAAirHi9gkrbrdQFiZCAABQjuI51hoeMssAAIBDw4oQAAAUY90N9baFiRAAABRjXYo128KpUQAAcGhYEQIAgHLsMFgGEyEAAChGJf+an83PpGIiBAAAxUh1neU+pYEGU0+4RggAAA4NK0IAAFCOFdcIbQ0TIQAAKEf+fYQ2nzhxahQAABwaVoQAAKAYJN0GAADHhmuEAADgyFTCihVhA42lvnCNEAAAHBpWhAAAoBg7zLCGiRAAABRkhzMhTo0CAIBDw4oQAAAUo1JZcTsEbp8AAIA7BirUAwCAQ8M1QgAAAPuCFSEAACjGmhvqbb0kxEQIAACKUaH6BAAAgH3BRAgAAA4Np0YBAEAxVpVhaqCx1BcmQgAAUIwV1whtPQ/i1CgAADg2rAgBAEA5dnhDPSZCAABQjvxrhDa/SIhTowAA4NCwIgQAAMWo5C/wbL0gxEQIAADKseb2CVtfJGykidDJyWnDO0sObf68cX5dI7t48WJwcLBarbb1QBrWxYsXW7Ro4eR0h59Od5DdzMzMbNmypSPsZqtWreSXx7Mzjbab/3nwwcWLF9+4z5BQP7mbdb0QkKHTWTsoBahMJlPj/KbMzEyDwdA4v6uRVVVV6Wz6LjYO7OadBLt5J2m03QwMDHR1dVV8syaTqbq62obvVONNhAAAALehO/zECAAAwI1hIgQAAIeGiRAAABwaJkIAAHBouI9QtqtXrx4/fjwrK2vIkCFt2rQxt2dmZq5bt66srGzixIndu3e34QgVcfLkyb179167di0yMjIuLs4cKlZcXLxmzZqsrKxBgwaNGTPGtoO8dfv27UtKSioqKmrVqtXUqVN9fX2l9qKiojVr1mRnZw8dOnTUqFG2HaSCNm/erNPpxo4dK31bVVUVHx//xx9/dOnSJS4uzt7vpti5c2dOTo70uGnTphMmTJAeFxQUfPrppzk5OSNGjBg+fLjtBqiY3Nzc9evXX7lypXXr1tOnT/fy8hIWB+2QIUNGjx5t6zHaE/s+7m2if//+b7311vz5848fP25uzMnJ6d69e1FRkb+//7BhwxITE204wltXVFQUGxt77dq1Vq1abdy4sX///lVVVUIIo9E4ePDgpKSkNm3azJ0794MPPrD1SG/V119/bTQaw8LCfv75586dOxcUFAgh9Hr9gAEDjh8/HhYW9tRTT61cudLWw1TGzp07H3/88eXLl5tbHnjggS1btoSHh7/77rsvvPCCDcemiOXLl+/du/fChQsXLly4fPmy1FhdXd2vX7/k5OTWrVvPmDFj3bp1Nh2jAs6dOxcdHX327NnQ0NC0tDRpT/V6/cCBA48fP96mTZtnnnnmk08+sfUw7YoJZDIYDCaTKSYm5quvvjI3vvnmm+PGjZMeL1++fNSoUbYZnEIMBkNVVZX0uKKiwsvLKzEx0WQy7d69OzQ0tKamxmQy/fTTT0FBQdLjO4DRaGzduvX27dtNJtP27dsjIiKkN1raZemxXSsqKoqKinrzzTf79Okjtfz666/u7u7FxcUmk+n8+fOurq75+fk2HeOt6tu3786dO2s1fvnllx07djQajSaTafv27eHh4dJj+zVixIhXXnmlVqO0a9KB+sMPP4SEhOj1eluMzi5hRSgbefooMTHRfMpl2LBhhw4datxBKczJycnZ2Vl6bDQaq6urPT09hRCHDh26++67NRqNEGLgwIH5+flpaWm2HKhy0tLSioqK2rdvL4Q4dOjQkCFDpDd6yJAhFy9ezMjIsPH4btm8efPmzZsXFBRkbklMTOzZs6f0zoaFhQUHBx89etR2A1TGDz/88I9//GP37t2m/94hnZiYOHToUCnryvDhw8+dO3flyhWbjvGW1NTUJCQkjB07du3atStXrjQvfGsdtJcvX74DDtpGg4lQGdnZ2c2aNZMe+/v7l5WVFRcX23ZISnnxxRcHDBjQuXNnIUROTo55N9Vqta+vb3Z2tk1Hp4CXXnopODg4Ojr6nXfekSZCy3fT2dnZx8fH3nfzp59+Sk9PnzFjhmWj5bsphPD397frGUII0aFDB51Ol5ubO3v27NjYWKPRKP76brq5uXl4eNj1u3np0iWj0fj0009nZGScOXMmJibmt99+E399N7Va7R1w0DYmBMsoQ6PR6PV66bH0QKvV2nREynj//fcTEhLMlzw1Go1lnryamhrzwtF+vfHGG88999zhw4effPLJTp06de/eXavV3km7WVZWNnv27G3bttXKRXnnvZurV6+WHixYsCAiImLv3r0jRoyw/GwKIfR6vV3vppOTk8lkevrpp6V/a2pqav7xj3/Ex8ffee9mY8KKUBnBwcHm/6azsrKaNm3aEBn5Gtk///nPjz76aP/+/QEBAVJLcHBwVlaW9LiioqKwsNDyVJudcnd3DwgImDhx4ogRI7799lvx190sKSkpKSmx6908dOhQVlbWQw891K1btyVLliQnJ3fr1s1oNFruphAiKyvLrnfTko+PT4cOHdLT08VfP5t5eXmVlZV2vZuBgYFOTk4dOnSQvo2KisrMzBR1Dtri4mK73s1GholQGbGxsVu3bpVOxXzzzTexsbG2HtGt+vTTT999992EhIQWLVqYG2NjYxMSEoqKioQQ27dvb9euneUNJHZHr9fX1NRIj6urq5OTk1u1aiWEiI2N3bNnT0lJiRBiy5YtXbp0CQ4OtuVAb03fvn3379+/atWqVatWTZ06NSwsbNWqVU5OTiNGjPjPf/4j/Rn95Zdfqqqq+vTpY+vBWq+mpsa88rt06dKpU6eioqKEELGxsbt37y4vLxdCbNmypXfv3n5+sssj3D50Ot3IkSOPHDkifXvkyBFpUoyNjf3xxx+lg3br1q2dO3e2/OTCTdg6Wsf+PPPMM127dnV1dQ0LC+vatevx48dNJlNpaeldd901YMCASZMmNW/ePDU11dbDvCVZWVkqlapVq1Zd/+v777+XfhQXF9ehQ4eHH37Yz8/vu+++s+04b9HFixebN28+bty4uLi4kJCQoUOHVlRUSD+aMGFCx44dp0+f7ufn9+OPP9p2nApas2aNOWrUZDK98soroaGhM2bMCAgIWLVqlQ0HduvOnz8fGBh4//33T5o0ycfH5+mnn5bajUZjbGxs586dp02b5uvre+DAAZsOUwEnT5709/efNm3aqFGj2rZte+XKFal94sSJ5oN2z549th2kfUH1CdnOnTtnGQgTEREhxd1VVVXt37+/tLR06NChPj4+thugAqqrq8+cOWPZEhoaKt1sbjKZDh8+nJWV1adPn5CQEBsNUDEXL148depUZWVleHh4ly5dzO0mk+nQoUM5OTl9+/Zt2bKlDUeorLy8vPz8/Hbt2plbjh8/fu7cuc6dO0dGRtpwYIpISUlJSUkxGo3R0dERERHmdqPRePDgwdzc3P79+9v14t4sPz9///793t7e/fr1M1+FMZlMiYmJ2dnZd9hB2wgwEQIAgEPDNUIAAHBomAgBAMChYSIEAACHhokQAAAcGiZCAABwaJgIAQDAoWEiBAAAh4aJEAAAHBomQgAAcGiYCAEAwKFhIgQAAIf2/8B1bSZqbDjCAAAAAElFTkSuQmCC", + "image/png": 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a/dev/examples/gross_pitaevskii_2D/8610f549.svg +++ /dev/null @@ -1,511 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/dev/examples/gross_pitaevskii_2D/index.html b/dev/examples/gross_pitaevskii_2D/index.html index e06da06691..30b64d41cb 100644 --- a/dev/examples/gross_pitaevskii_2D/index.html +++ b/dev/examples/gross_pitaevskii_2D/index.html @@ -30,4 +30,4 @@ model = Model(lattice; n_electrons, terms, spin_polarization=:spinless) # spinless electrons basis = PlaneWaveBasis(model; Ecut, kgrid=(1, 1, 1)) scfres = direct_minimization(basis, tol=1e-5) # Reduce tol for production -heatmap(scfres.ρ[:, :, 1, 1], c=:blues)Example block output +heatmap(scfres.ρ[:, :, 1, 1], c=:blues)Example block output diff --git a/dev/examples/input_output.ipynb b/dev/examples/input_output.ipynb index 44a7f23a33..a3caf1d62e 100644 --- a/dev/examples/input_output.ipynb +++ b/dev/examples/input_output.ipynb @@ -104,22 +104,22 @@ "text": [ "n Energy log10(ΔE) log10(Δρ) Magnet Diag Δtime\n", "--- --------------- --------- --------- ------ ---- ------\n", - " 1 -223.7491095755 0.22 -6.321 5.8 \n", - " 2 -224.1603662755 -0.39 -0.21 -3.326 1.7 191ms\n", - " 3 -224.2175860392 -1.24 -1.05 -1.615 3.3 319ms\n", - " 4 -224.2198724211 -2.64 -1.46 -1.193 1.1 175ms\n", - " 5 -224.2207936187 -3.04 -1.72 -0.823 1.0 171ms\n", - " 6 -224.2212100446 -3.38 -2.00 -0.489 1.0 172ms\n", - " 7 -224.2213736756 -3.79 -2.35 -0.228 1.2 178ms\n", - " 8 -224.2214156503 -4.38 -2.89 -0.075 2.0 199ms\n", - " 9 -224.2214205665 -5.31 -3.56 -0.007 2.5 265ms\n", - " 10 -224.2214206928 -6.90 -3.82 0.001 3.1 275ms\n", - " 11 -224.2214207113 -7.73 -4.06 -0.001 2.4 189ms\n", - " 12 -224.2214207181 -8.17 -4.46 0.000 1.7 200ms\n", - " 13 -224.2214207187 -9.21 -5.01 0.000 2.2 223ms\n", - " 14 -224.2214207187 -10.37 -5.38 -0.000 2.4 215ms\n", - " 15 -224.2214207187 -11.02 -5.82 -0.000 1.8 220ms\n", - " 16 -224.2214207187 -12.59 -6.24 0.000 2.3 235ms\n" + " 1 -223.7489534751 0.22 -6.319 5.6 \n", + " 2 -224.1606292516 -0.39 -0.21 -3.325 1.9 328ms\n", + " 3 -224.2176006809 -1.24 -1.06 -1.615 3.2 432ms\n", + " 4 -224.2198729514 -2.64 -1.46 -1.193 1.1 298ms\n", + " 5 -224.2207934216 -3.04 -1.72 -0.823 1.0 292ms\n", + " 6 -224.2212102838 -3.38 -2.00 -0.488 1.1 358ms\n", + " 7 -224.2213737278 -3.79 -2.35 -0.228 1.7 309ms\n", + " 8 -224.2214154367 -4.38 -2.88 -0.076 1.9 313ms\n", + " 9 -224.2214205574 -5.29 -3.56 -0.007 2.4 353ms\n", + " 10 -224.2214206908 -6.87 -3.82 0.001 3.6 427ms\n", + " 11 -224.2214207112 -7.69 -4.07 -0.001 2.2 370ms\n", + " 12 -224.2214207176 -8.19 -4.39 -0.000 1.7 321ms\n", + " 13 -224.2214207187 -8.97 -5.05 0.000 2.2 330ms\n", + " 14 -224.2214207187 -10.45 -5.40 0.000 2.2 337ms\n", + " 15 -224.2214207187 -10.98 -5.85 -0.000 2.6 376ms\n", + " 16 -224.2214207187 -13.07 -6.27 0.000 1.9 349ms\n" ] } ], @@ -207,15 +207,15 @@ "output_type": "stream", "text": [ "{\n", - " \"AtomicNonlocal\": -4.345930955510355,\n", + " \"AtomicNonlocal\": -4.345930946982073,\n", " \"PspCorrection\": 7.03000636467455,\n", " \"Ewald\": -161.52650757200072,\n", - " \"total\": -224.22142071873432,\n", - " \"Entropy\": -0.030647820074375248,\n", - " \"Kinetic\": 75.6630496206195,\n", - " \"AtomicLocal\": -161.12810848098508,\n", - " \"Hartree\": 41.397006598348284,\n", - " \"Xc\": -21.280288473806163\n", + " \"total\": -224.22142071873435,\n", + " \"Entropy\": -0.030647819547704954,\n", + " \"Kinetic\": 75.66304948120228,\n", + " \"AtomicLocal\": -161.1281082576384,\n", + " \"Hartree\": 41.39700649067214,\n", + " \"Xc\": -21.280288459114402\n", "}\n" ] } diff --git a/dev/examples/input_output/index.html b/dev/examples/input_output/index.html index faa931f244..2ae3a18583 100644 --- a/dev/examples/input_output/index.html +++ b/dev/examples/input_output/index.html @@ -37,29 +37,29 @@ ρ0 = guess_density(basis, system) scfres = self_consistent_field(basis, ρ=ρ0);
    n     Energy            log10(ΔE)   log10(Δρ)   Magnet   Diag   Δtime
     ---   ---------------   ---------   ---------   ------   ----   ------
    -  1   -223.7493649075                    0.22   -6.320    5.6
    -  2   -224.1604158047       -0.39       -0.21   -3.326    1.8    152ms
    -  3   -224.2175799255       -1.24       -1.05   -1.615    3.2    222ms
    -  4   -224.2198714234       -2.64       -1.46   -1.193    1.1    140ms
    -  5   -224.2207933679       -3.04       -1.72   -0.824    1.0    137ms
    -  6   -224.2212112627       -3.38       -2.00   -0.487    1.2    139ms
    -  7   -224.2213738672       -3.79       -2.35   -0.228    1.7    145ms
    -  8   -224.2214155320       -4.38       -2.88   -0.076    1.8    188ms
    -  9   -224.2214205655       -5.30       -3.55   -0.007    2.5    166ms
    - 10   -224.2214206909       -6.90       -3.81    0.001    3.6    189ms
    - 11   -224.2214207109       -7.70       -4.05   -0.001    1.9    152ms
    - 12   -224.2214207181       -8.14       -4.48    0.000    1.7    150ms
    - 13   -224.2214207187       -9.30       -4.98    0.000    2.2    175ms
    - 14   -224.2214207187      -10.15       -5.40   -0.000    1.7    150ms
    - 15   -224.2214207187      -11.02       -5.82   -0.000    2.9    171ms
    - 16   -224.2214207187      -12.40       -6.21    0.000    2.2    171ms

    Writing VTK files for visualization

    For visualizing the density or the Kohn-Sham orbitals DFTK supports storing the result of an SCF calculations in the form of VTK files. These can afterwards be visualized using tools such as paraview. Using this feature requires the WriteVTK.jl Julia package.

    using WriteVTK
    +  1   -223.7490925181                    0.22   -6.319    5.3
    +  2   -224.1605115735       -0.39       -0.21   -3.325    2.0    324ms
    +  3   -224.2175810212       -1.24       -1.06   -1.616    2.9    458ms
    +  4   -224.2198749506       -2.64       -1.46   -1.192    1.1    296ms
    +  5   -224.2207941941       -3.04       -1.73   -0.823    1.0    292ms
    +  6   -224.2212148323       -3.38       -2.00   -0.483    1.1    293ms
    +  7   -224.2213746789       -3.80       -2.35   -0.226    1.5    308ms
    +  8   -224.2214158294       -4.39       -2.90   -0.073    2.5    377ms
    +  9   -224.2214205734       -5.32       -3.55   -0.007    2.4    352ms
    + 10   -224.2214206904       -6.93       -3.81    0.001    3.0    395ms
    + 11   -224.2214207106       -7.69       -4.04   -0.001    2.8    341ms
    + 12   -224.2214207182       -8.12       -4.48    0.000    1.5    317ms
    + 13   -224.2214207186       -9.34       -4.98    0.000    2.2    389ms
    + 14   -224.2214207187       -9.97       -5.38   -0.000    1.6    317ms
    + 15   -224.2214207187      -10.99       -5.76   -0.000    1.8    322ms
    + 16   -224.2214207187      -12.07       -6.14    0.000    3.0    385ms

    Writing VTK files for visualization

    For visualizing the density or the Kohn-Sham orbitals DFTK supports storing the result of an SCF calculations in the form of VTK files. These can afterwards be visualized using tools such as paraview. Using this feature requires the WriteVTK.jl Julia package.

    using WriteVTK
     save_scfres("iron_afm.vts", scfres; save_ψ=true);

    This will save the iron calculation above into the file iron_afm.vts, using save_ψ=true to also include the KS orbitals.

    Parsable data-export using json

    Many structures in DFTK support the (unexported) todict function, which returns a simplified dictionary representation of the data.

    DFTK.todict(scfres.energies)
    Dict{String, Float64} with 9 entries:
       "AtomicNonlocal" => -4.34593
       "PspCorrection"  => 7.03001
       "Ewald"          => -161.527
       "total"          => -224.221
       "Entropy"        => -0.0306478
    -  "Kinetic"        => 75.663
    +  "Kinetic"        => 75.6631
       "AtomicLocal"    => -161.128
       "Hartree"        => 41.397
       "Xc"             => -21.2803

    This in turn can be easily written to disk using a JSON library. Currently we integrate most closely with JSON3, which is thus recommended.

    using JSON3
    @@ -67,17 +67,17 @@
         JSON3.pretty(io, DFTK.todict(scfres.energies))
     end
     println(read("iron_afm_energies.json", String))
    {
    -    "AtomicNonlocal": -4.345930961156931,
    +    "AtomicNonlocal": -4.345930967927003,
         "PspCorrection": 7.03000636467455,
         "Ewald": -161.52650757200072,
    -    "total": -224.2214207187345,
    -    "Entropy": -0.030647819582185307,
    -    "Kinetic": 75.6630499816012,
    -    "AtomicLocal": -161.12810925618496,
    -    "Hartree": 41.39700706836531,
    -    "Xc": -21.280288524450768
    +    "total": -224.22142071873458,
    +    "Entropy": -0.030647818483937852,
    +    "Kinetic": 75.66305043148016,
    +    "AtomicLocal": -161.1281103963094,
    +    "Hartree": 41.397007834745466,
    +    "Xc": -21.28028859491369
     }

    Once JSON3 is loaded, additionally a convenience function for saving a parsable summary of scfres objects using save_scfres is available:

    using JSON3
     save_scfres("iron_afm.json", scfres)

    Writing and reading JLD2 files

    The full state of a DFTK self-consistent field calculation can be stored on disk in form of an JLD2.jl file. This file can be read from other Julia scripts as well as other external codes supporting the HDF5 file format (since the JLD2 format is based on HDF5).

    using JLD2
     save_scfres("iron_afm.jld2", scfres);

    Since such JLD2 can also be read by DFTK to start or continue a calculation, these can also be used for checkpointing or for transferring results to a different computer. See Saving SCF results on disk and SCF checkpoints for details.

    (Cleanup files generated by this notebook.)

    rm("iron_afm.vts")
     rm("iron_afm.jld2")
    -rm("iron_afm_energies.json")
    +rm("iron_afm_energies.json") diff --git a/dev/examples/iron_afm.json b/dev/examples/iron_afm.json index 0d28392940..b4ddcea0da 100644 --- a/dev/examples/iron_afm.json +++ b/dev/examples/iron_afm.json @@ -14,19 +14,19 @@ 0.9999999999999993, 0.9999999999994962, 0.9999999998841886, - 0.9999551797273162, - 0.9999551797273162, - 0.9991867275082167, - 1.2376215559044393e-6, - 7.661675832269909e-7, - 7.661675832269486e-7, - 9.1491909013454e-8, - 9.149190901345498e-8, - 5.784373011392944e-16, - 1.1323764870841121e-20, - 3.1347720439041666e-28, - 1.3258421878872888e-30, - 1.0054653874177945e-30 + 0.9999551797839473, + 0.9999551797839473, + 0.999186727377449, + 1.2376219497539984e-6, + 7.66168262270085e-7, + 7.661682622699831e-7, + 9.149204350529177e-8, + 9.149204350529177e-8, + 5.784400157251462e-16, + 1.132379546019176e-20, + 3.1347864121577454e-28, + 1.2980478502947972e-30, + 1.0059893056335185e-30 ], [ 1, @@ -39,21 +39,21 @@ 1, 1, 1, - 0.9999931508390929, - 0.999993150838709, - 0.9999919978490494, - 0.9999919978483868, - 0.9696446262220028, - 0.969644624090166, - 0.6530155280914054, - 0.6530155066728698, - 0.008456051446243939, - 0.008456050560646746, - 6.269133417996811e-23, - 6.269133287708764e-23, - 3.5342426479534013e-28, - 3.103375578608667e-28, - 1.1147172461642082e-28 + 0.9999931508545388, + 0.9999931508533393, + 0.99999199785788, + 0.9999919978558096, + 0.969644659006075, + 0.9696446523455631, + 0.6530163442644873, + 0.6530162773462138, + 0.0084560632049748, + 0.008456060438085708, + 6.269133775940482e-23, + 6.269133368879675e-23, + 3.534242894288316e-28, + 3.499095359741927e-28, + 1.1147177618974574e-28 ], [ 1, @@ -66,21 +66,21 @@ 1, 1, 1, - 0.9999998989589967, - 0.9999998989589967, - 0.9999994082178694, - 0.9999994082178694, - 0.9999961224141392, - 0.999996122413843, - 0.0006727033619342288, - 0.0006727032891097776, - 2.984194932160575e-12, - 2.9841949321606066e-12, - 2.322000739100922e-22, - 2.322000739100394e-22, - 5.040838645321029e-23, - 5.0408371731406815e-23, - 1.2833423843225125e-25 + 0.9999998989591077, + 0.9999998989591077, + 0.9999994082185375, + 0.9999994082185375, + 0.9999961224166407, + 0.9999961224157154, + 0.0006727062306914371, + 0.0006727060031643748, + 2.9841993933861823e-12, + 2.98419939338562e-12, + 2.322002205240285e-22, + 2.322002205239806e-22, + 5.040839992581694e-23, + 5.0408392058442044e-23, + 1.2833460006087033e-25 ], [ 1, @@ -95,19 +95,19 @@ 0.9999999999999858, 0.999999999998956, 0.999999999996529, - 0.9999925866319139, - 0.9999925866319139, - 0.600207909666485, - 1.3204374572889474e-5, - 2.5105525426218265e-8, - 2.5105525426217997e-8, - 1.2023170786684428e-14, - 1.202317078668417e-14, - 3.686409745305754e-17, - 6.678195920483556e-18, - 4.818826388747126e-18, - 2.94024110746216e-27, - 9.660154930167283e-31 + 0.9999925866381861, + 0.9999925866381861, + 0.6002080691235002, + 1.3204379313552326e-5, + 2.510555918521335e-8, + 2.5105559185212193e-8, + 1.2023189911421872e-14, + 1.2023189911421872e-14, + 3.686406664016156e-17, + 6.6782266677857354e-18, + 4.818825490576378e-18, + 2.940215336792712e-27, + 9.660178430071076e-31 ], [ 1, @@ -122,19 +122,19 @@ 0.9999999999999998, 0.9999999999722371, 0.9999999999722371, - 0.9999501565646841, - 0.9999501565616356, - 0.634552894162312, - 0.6345528727745053, - 0.5380300583807933, - 0.5380300357042441, - 0.0006621779020495482, - 0.0006621778214219845, - 1.0317589698704033e-11, - 1.0317589483007725e-11, - 6.154372950309905e-31, - 6.154361345408694e-31, - 2.170162801403807e-39 + 0.9999501566840965, + 0.9999501566745715, + 0.6345531404678932, + 0.6345530736456317, + 0.5380309032217758, + 0.5380308323731533, + 0.0006621786423201327, + 0.0006621783904143, + 1.0317591601687392e-11, + 1.0317590927780434e-11, + 6.15437150449639e-31, + 6.154329538550899e-31, + 2.169894965983753e-39 ], [ 1, @@ -149,19 +149,19 @@ 0.9999999999999998, 0.9999999999993692, 0.9999999999993692, - 0.9999913381168228, - 0.9999913381168228, - 0.29776963757120284, - 0.29776961341237596, - 0.2031314273465689, - 0.20313140660734202, - 0.0901925468641624, - 0.09019254248793199, - 7.401559103936206e-6, - 7.401559103936206e-6, - 1.669254445328521e-20, - 1.66925437044739e-20, - 1.1675022775441043e-26 + 0.9999913381338023, + 0.9999913381338023, + 0.2977705636301416, + 0.29777048815024065, + 0.20313154780930237, + 0.20313148301343764, + 0.09019250396893916, + 0.09019249029619837, + 7.4015702888357464e-6, + 7.401570288835918e-6, + 1.669259290990539e-20, + 1.6692591276941988e-20, + 1.16750222538898e-26 ], [ 1, @@ -175,20 +175,20 @@ 1, 0.9999999999999993, 0.9999999999994962, - 0.9999999998841882, - 0.9999551795178363, - 0.9999551795178363, - 0.9991867256707742, - 1.2376168903916976e-6, - 7.661642069477214e-7, - 7.661642069477037e-7, - 9.149147911641343e-8, - 9.149147911641635e-8, - 5.784311944097426e-16, - 1.1323722647332268e-20, - 3.1347550385301383e-28, - 1.3260893460531705e-30, - 1.0054627619905933e-30 + 0.9999999998841878, + 0.9999551794096034, + 0.9999551794096034, + 0.999186723431268, + 1.2376139467211763e-6, + 7.661627394048839e-7, + 7.661627394047641e-7, + 9.149125656928715e-8, + 9.149125656928715e-8, + 5.784299492749004e-16, + 1.1323718854293635e-20, + 3.134749689802932e-28, + 1.3261112892053921e-30, + 1.005430155140901e-30 ], [ 1, @@ -201,21 +201,21 @@ 1, 1, 1, - 0.9999931508080325, - 0.9999931508076487, - 0.9999919978144922, - 0.9999919978138294, - 0.9696444932768846, - 0.9696444911450413, - 0.653013599868982, - 0.6530135784503579, - 0.008456011347119843, - 0.008456010461525979, - 6.26911553340638e-23, - 6.269115403119417e-23, - 3.5342316647626115e-28, - 1.6627416008046867e-28, - 1.1147130047667386e-28 + 0.9999931508012243, + 0.9999931508000248, + 0.9999919977953721, + 0.9999919977933015, + 0.9696444231345173, + 0.9696444164739528, + 0.6530131518556437, + 0.6530130849371395, + 0.008455991477433427, + 0.008455988710568909, + 6.269102688667828e-23, + 6.269102281610866e-23, + 3.534224759276158e-28, + 1.124206527623761e-28, 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-2.4550124753050797, - -2.453169788979519, - -2.4531697889795194, - -2.439529191596309, - -2.43952919159631, - 0.3474945546009795, - 0.3474945546178996, - 0.4320380035194596, - 0.4320380035194591, - 0.5963914448616897, - 0.5963914448616907, - 0.7215365984612054, - 0.7215365996165609, - 0.7266254822453825, - 0.7266254835266182, - 0.7360699697494829, - 0.7360699702827951, - 0.8310952552251233, - 0.8310952552251232, - 1.1683503849756691, - 1.1683503852865038, - 1.310080564385916 + -3.9733540974268693, + -3.97335409128004, + -2.455012475108835, + -2.455012469302238, + -2.4531697987854737, + -2.453169798785475, + -2.439529202366999, + -2.4395292023670008, + 0.3474945597404726, + 0.3474945597933373, + 0.43203801965755867, + 0.43203801965755906, + 0.5963914559733069, + 0.5963914559733067, + 0.7215366112424018, + 0.7215366148520967, + 0.7266255002358766, + 0.7266255042388552, + 0.7360699852401376, + 0.73606998690637, + 0.8310952709633108, + 0.831095270963311, + 1.168350386250433, + 1.1683503872366425, + 1.3100805759432417 ] ], "damping": 0.8, diff --git a/dev/examples/metallic_systems.ipynb b/dev/examples/metallic_systems.ipynb index 5c2bcb2201..e4aa50d2a1 100644 --- a/dev/examples/metallic_systems.ipynb +++ b/dev/examples/metallic_systems.ipynb @@ -92,13 +92,13 @@ "text": [ "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -1.743062056252 -1.29 4.3 \n", - " 2 -1.743507550754 -3.35 -1.70 2.2 34.7ms\n", - " 3 -1.743614178141 -3.97 -2.89 4.3 60.3ms\n", - " 4 -1.743616733998 -5.59 -3.71 4.3 39.1ms\n", - " 5 -1.743616749581 -7.81 -4.64 2.8 44.6ms\n", - " 6 -1.743616749877 -9.53 -5.56 3.2 32.6ms\n", - " 7 -1.743616749884 -11.12 -6.63 3.7 47.0ms\n" + " 1 -1.743070702605 -1.29 5.0 \n", + " 2 -1.743505476323 -3.36 -1.70 1.3 66.3ms\n", + " 3 -1.743613936325 -3.96 -2.84 4.7 113ms\n", + " 4 -1.743616730639 -5.55 -3.60 3.8 80.9ms\n", + " 5 -1.743616748984 -7.74 -4.50 3.3 113ms\n", + " 6 -1.743616749878 -9.05 -5.38 3.3 74.3ms\n", + " 7 -1.743616749884 -11.21 -6.50 3.3 95.7ms\n" ] } ], @@ -114,7 +114,7 @@ { "output_type": "execute_result", "data": { - "text/plain": "9-element Vector{Float64}:\n 1.9999999999941416\n 1.9985518374675462\n 1.990551434051823\n 1.2449689666183935e-17\n 1.2448837418504073e-17\n 1.0289491088741036e-17\n 1.0288615201487746e-17\n 2.98841882224761e-19\n 1.6623262890942646e-21" + "text/plain": "9-element Vector{Float64}:\n 1.9999999999941416\n 1.998551837435689\n 1.9905514353133948\n 1.244967446947794e-17\n 1.2448822223204663e-17\n 1.0289484513080082e-17\n 1.0288608626151876e-17\n 2.9884241498988145e-19\n 1.662371586873889e-21" }, "metadata": {}, "execution_count": 4 @@ -132,7 +132,7 @@ { "output_type": "execute_result", "data": { - "text/plain": "Energy breakdown (in Ha):\n Kinetic 0.7450615 \n AtomicLocal 0.3193179 \n AtomicNonlocal 0.3192776 \n Ewald -2.1544222\n PspCorrection -0.1026056\n Hartree 0.0061603 \n Xc -0.8615676\n Entropy -0.0148387\n\n total -1.743616749884" + "text/plain": "Energy breakdown (in Ha):\n Kinetic 0.7450614 \n AtomicLocal 0.3193179 \n AtomicNonlocal 0.3192777 \n Ewald -2.1544222\n PspCorrection -0.1026056\n Hartree 0.0061603 \n Xc -0.8615676\n Entropy -0.0148387\n\n total -1.743616749884" }, "metadata": {}, "execution_count": 5 @@ -159,122 +159,122 @@ "output_type": "execute_result", "data": { "text/plain": "Plot{Plots.GRBackend() n=2}", - "image/png": 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", + "image/png": 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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/examples/metallic_systems/index.html b/dev/examples/metallic_systems/index.html index fa74d5a173..8d3dd666ce 100644 --- a/dev/examples/metallic_systems/index.html +++ b/dev/examples/metallic_systems/index.html @@ -24,23 +24,23 @@ kgrid = kgrid_from_minimal_spacing(lattice, kspacing) basis = PlaneWaveBasis(model; Ecut, kgrid);

    Finally we run the SCF. Two magnesium atoms in our pseudopotential model result in four valence electrons being explicitly treated. Nevertheless this SCF will solve for eight bands by default in order to capture partial occupations beyond the Fermi level due to the employed smearing scheme. In this example we use a damping of 0.8. The default LdosMixing should be suitable to converge metallic systems like the one we model here. For the sake of demonstration we still switch to Kerker mixing here.

    scfres = self_consistent_field(basis, damping=0.8, mixing=KerkerMixing());
    n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime
     ---   ---------------   ---------   ---------   ----   ------
    -  1   -1.743032456226                   -1.28    5.3
    -  2   -1.743505881062       -3.32       -1.70    1.3   22.7ms
    -  3   -1.743613448050       -3.97       -2.82    4.0   35.3ms
    -  4   -1.743616718984       -5.49       -3.52    3.8   35.6ms
    -  5   -1.743616748922       -7.52       -4.40    2.7   30.2ms
    -  6   -1.743616749874       -9.02       -5.50    3.2   33.7ms
    -  7   -1.743616749884      -11.00       -6.20    4.2   41.0ms
    scfres.occupation[1]
    9-element Vector{Float64}:
    +  1   -1.742967267480                   -1.29    5.5
    +  2   -1.743502383028       -3.27       -1.70    1.0   41.2ms
    +  3   -1.743613808736       -3.95       -2.83    4.0   69.8ms
    +  4   -1.743616723926       -5.54       -3.63    4.2   75.7ms
    +  5   -1.743616749144       -7.60       -4.54    3.0   66.2ms
    +  6   -1.743616749877       -9.13       -5.37    3.3   67.9ms
    +  7   -1.743616749884      -11.13       -6.35    3.2   64.5ms
    scfres.occupation[1]
    9-element Vector{Float64}:
      1.9999999999941416
    - 1.9985518379616314
    - 1.9905514366446095
    - 1.2449706831758e-17
    - 1.2448854582684316e-17
    - 1.0289507399543557e-17
    - 1.0288631511122962e-17
    - 2.988418436535361e-19
    - 1.6622563936388916e-21
    scfres.energies
    Energy breakdown (in Ha):
    -    Kinetic             0.7450615 
    + 1.9985518371175794
    + 1.990551440266694
    + 1.2449680166662614e-17
    + 1.2448827919677096e-17
    + 1.0289502611930192e-17
    + 1.0288626723900546e-17
    + 2.988419410160129e-19
    + 1.6623405043128506e-21
    scfres.energies
    Energy breakdown (in Ha):
    +    Kinetic             0.7450614 
         AtomicLocal         0.3193179 
         AtomicNonlocal      0.3192776 
         Ewald               -2.1544222
    @@ -49,4 +49,4 @@
         Xc                  -0.8615676
         Entropy             -0.0148387
     
    -    total               -1.743616749884

    The fact that magnesium is a metal is confirmed by plotting the density of states around the Fermi level.

    plot_dos(scfres)
    Example block output + total -1.743616749884

    The fact that magnesium is a metal is confirmed by plotting the density of states around the Fermi level.

    plot_dos(scfres)
    Example block output diff --git a/dev/examples/polarizability.ipynb b/dev/examples/polarizability.ipynb index 7d06734718..2c32cf4888 100644 --- a/dev/examples/polarizability.ipynb +++ b/dev/examples/polarizability.ipynb @@ -67,23 +67,23 @@ "text": [ "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -2.770415905290 -0.53 9.0 \n", - " 2 -2.771673456279 -2.90 -1.30 1.0 132ms\n", - " 3 -2.771712765901 -4.41 -2.68 1.0 89.2ms\n", - " 4 -2.771714702826 -5.71 -3.57 2.0 137ms\n", - " 5 -2.771714714047 -7.95 -4.00 2.0 121ms\n", - " 6 -2.771714715229 -8.93 -5.56 1.0 93.7ms\n", - " 7 -2.771714715249 -10.69 -5.76 3.0 127ms\n", - " 8 -2.771714715250 -12.47 -6.13 1.0 97.4ms\n", - " 9 -2.771714715250 -13.53 -6.99 1.0 114ms\n", - " 10 -2.771714715250 -14.88 -7.28 2.0 119ms\n", - " 11 -2.771714715250 -14.75 -8.83 1.0 107ms\n" + " 1 -2.770236417592 -0.53 8.0 \n", + " 2 -2.771684994515 -2.84 -1.30 1.0 170ms\n", + " 3 -2.771714246707 -4.53 -2.66 1.0 229ms\n", + " 4 -2.771714710411 -6.33 -3.79 2.0 274ms\n", + " 5 -2.771714714870 -8.35 -4.22 2.0 205ms\n", + " 6 -2.771714715240 -9.43 -5.40 1.0 219ms\n", + " 7 -2.771714715250 -11.02 -6.26 2.0 227ms\n", + " 8 -2.771714715250 -13.60 -6.76 1.0 203ms\n", + " 9 -2.771714715250 -13.73 -7.48 2.0 248ms\n", + " 10 -2.771714715250 + -Inf -7.56 1.0 210ms\n", + " 11 -2.771714715250 + -14.35 -8.73 1.0 213ms\n" ] }, { "output_type": "execute_result", "data": { - "text/plain": "-0.00013457423020209696" + "text/plain": "-0.00013457372509827502" }, "metadata": {}, "execution_count": 2 @@ -114,24 +114,22 @@ "text": [ "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -2.770548898305 -0.52 9.0 \n", - " 2 -2.771781281856 -2.91 -1.32 1.0 115ms\n", - " 3 -2.771801707797 -4.69 -2.46 1.0 109ms\n", - " 4 -2.771802009944 -6.52 -3.17 1.0 131ms\n", - " 5 -2.771802074151 -7.19 -4.14 2.0 106ms\n", - " 6 -2.771802074328 -9.75 -4.35 1.0 119ms\n", - " 7 -2.771802074470 -9.85 -5.12 1.0 120ms\n", - " 8 -2.771802074476 -11.26 -5.73 1.0 113ms\n", - " 9 -2.771802074476 -12.28 -6.72 2.0 118ms\n", - " 10 -2.771802074476 + -14.10 -6.80 1.0 115ms\n", - " 11 -2.771802074476 -13.95 -7.54 1.0 112ms\n", - " 12 -2.771802074476 + -14.21 -8.14 1.0 118ms\n" + " 1 -2.770451867695 -0.53 8.0 \n", + " 2 -2.771768898824 -2.88 -1.31 1.0 178ms\n", + " 3 -2.771801022957 -4.49 -2.61 1.0 231ms\n", + " 4 -2.771802071340 -5.98 -4.05 2.0 252ms\n", + " 5 -2.771802074327 -8.52 -4.43 2.0 202ms\n", + " 6 -2.771802074468 -9.85 -5.35 1.0 223ms\n", + " 7 -2.771802074476 -11.11 -5.91 2.0 214ms\n", + " 8 -2.771802074476 -12.78 -6.66 1.0 229ms\n", + " 9 -2.771802074476 + -14.75 -7.14 1.0 191ms\n", + " 10 -2.771802074476 + -14.51 -8.46 2.0 271ms\n" ] }, { "output_type": "execute_result", "data": { - "text/plain": "0.017612224631147286" + "text/plain": "0.017612220906220825" }, "metadata": {}, "execution_count": 3 @@ -156,9 +154,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Reference dipole: -0.00013457423020209696\n", - "Displaced dipole: 0.017612224631147286\n", - "Polarizability : 1.7746798861349382\n" + "Reference dipole: -0.00013457372509827502\n", + "Displaced dipole: 0.017612220906220825\n", + "Polarizability : 1.77467946313191\n" ] } ], @@ -211,23 +209,23 @@ "output_type": "stream", "text": [ "WARNING: using KrylovKit.basis in module ##330 conflicts with an existing identifier.\n", - "[ Info: GMRES linsolve in iter 1; step 1: normres = 2.493920758093e-01\n", - "[ Info: GMRES linsolve in iter 1; step 2: normres = 3.766553665669e-03\n", - "[ Info: GMRES linsolve in iter 1; step 3: normres = 2.852770734248e-04\n", - "[ Info: GMRES linsolve in iter 1; step 4: normres = 4.694601539584e-06\n", - "[ Info: GMRES linsolve in iter 1; step 5: normres = 1.088784657239e-08\n", - "[ Info: GMRES linsolve in iter 1; step 6: normres = 6.275831374012e-11\n", - "[ Info: GMRES linsolve in iter 1; step 7: normres = 7.499595133522e-13\n", - "[ Info: GMRES linsolve in iter 1; finished at step 7: normres = 7.499595133522e-13\n", - "[ Info: GMRES linsolve in iter 2; step 1: normres = 6.320207484833e-10\n", - "[ Info: GMRES linsolve in iter 2; step 2: normres = 1.729485393550e-11\n", - "[ Info: GMRES linsolve in iter 2; step 3: normres = 1.270062387856e-12\n", - "[ Info: GMRES linsolve in iter 2; finished at step 3: normres = 1.270062387856e-12\n", + "[ Info: GMRES linsolve in iter 1; step 1: normres = 2.493920960853e-01\n", + "[ Info: GMRES linsolve in iter 1; step 2: normres = 3.766552896169e-03\n", + "[ Info: GMRES linsolve in iter 1; step 3: normres = 2.852750537269e-04\n", + "[ Info: GMRES linsolve in iter 1; step 4: normres = 4.694607013921e-06\n", + "[ Info: GMRES linsolve in iter 1; step 5: normres = 1.088792733561e-08\n", + "[ Info: GMRES linsolve in iter 1; step 6: normres = 6.341660867529e-11\n", + "[ Info: GMRES linsolve in iter 1; step 7: normres = 2.069236201373e-12\n", + "[ Info: GMRES linsolve in iter 1; finished at step 7: normres = 2.069236201373e-12\n", + "[ Info: GMRES linsolve in iter 2; step 1: normres = 1.039559477484e-09\n", + "[ Info: GMRES linsolve in iter 2; step 2: normres = 1.358666483445e-10\n", + "[ Info: GMRES linsolve in iter 2; step 3: normres = 1.744024008999e-12\n", + "[ Info: GMRES linsolve in iter 2; finished at step 3: normres = 1.744024008999e-12\n", "┌ Info: GMRES linsolve converged at iteration 2, step 3:\n", - "│ * norm of residual = 1.2701053732585969e-12\n", + "│ * norm of residual = 1.7440124725055367e-12\n", "└ * number of operations = 12\n", - "Non-interacting polarizability: 1.9257125367442822\n", - "Interacting polarizability: 1.7736548595648245\n" + "Non-interacting polarizability: 1.9257125317583443\n", + "Interacting polarizability: 1.7736548650802952\n" ] } ], diff --git a/dev/examples/polarizability/index.html b/dev/examples/polarizability/index.html index 6ae5f720b1..1c74d0f54f 100644 --- a/dev/examples/polarizability/index.html +++ b/dev/examples/polarizability/index.html @@ -20,19 +20,19 @@ end;

    Using finite differences

    We first compute the polarizability by finite differences. First compute the dipole moment at rest:

    model = model_LDA(lattice, atoms, positions; symmetries=false)
     basis = PlaneWaveBasis(model; Ecut, kgrid)
     res   = self_consistent_field(basis; tol)
    -μref  = dipole(basis, res.ρ)
    -0.00013457298248849408

    Then in a small uniform field:

    ε = .01
    +μref  = dipole(basis, res.ρ)
    -0.00013457351177135972

    Then in a small uniform field:

    ε = .01
     model_ε = model_LDA(lattice, atoms, positions;
                         extra_terms=[ExternalFromReal(r -> -ε * (r[1] - a/2))],
                         symmetries=false)
     basis_ε = PlaneWaveBasis(model_ε; Ecut, kgrid)
     res_ε   = self_consistent_field(basis_ε; tol)
    -με = dipole(basis_ε, res_ε.ρ)
    0.017612221694533286
    polarizability = (με - μref) / ε
    +με = dipole(basis_ε, res_ε.ρ)
    0.017612221524388648
    polarizability = (με - μref) / ε
     
     println("Reference dipole:  $μref")
     println("Displaced dipole:  $με")
    -println("Polarizability :   $polarizability")
    Reference dipole:  -0.00013457298248849408
    -Displaced dipole:  0.017612221694533286
    -Polarizability :   1.7746794677021778

    The result on more converged grids is very close to published results. For example DOI 10.1039/C8CP03569E quotes 1.65 with LSDA and 1.38 with CCSD(T).

    Using linear response

    Now we use linear response to compute this analytically; we refer to standard textbooks for the formalism. In the following, $χ_0$ is the independent-particle polarizability, and $K$ the Hartree-exchange-correlation kernel. We denote with $δV_{\rm ext}$ an external perturbing potential (like in this case the uniform electric field). Then:

    \[δρ = χ_0 δV = χ_0 (δV_{\rm ext} + K δρ),\]

    which implies

    \[δρ = (1-χ_0 K)^{-1} χ_0 δV_{\rm ext}.\]

    From this we identify the polarizability operator to be $χ = (1-χ_0 K)^{-1} χ_0$. Numerically, we apply $χ$ to $δV = -x$ by solving a linear equation (the Dyson equation) iteratively.

    using KrylovKit
    +println("Polarizability :   $polarizability")
    Reference dipole:  -0.00013457351177135972
    +Displaced dipole:  0.017612221524388648
    +Polarizability :   1.7746795036160008

    The result on more converged grids is very close to published results. For example DOI 10.1039/C8CP03569E quotes 1.65 with LSDA and 1.38 with CCSD(T).

    Using linear response

    Now we use linear response to compute this analytically; we refer to standard textbooks for the formalism. In the following, $χ_0$ is the independent-particle polarizability, and $K$ the Hartree-exchange-correlation kernel. We denote with $δV_{\rm ext}$ an external perturbing potential (like in this case the uniform electric field). Then:

    \[δρ = χ_0 δV = χ_0 (δV_{\rm ext} + K δρ),\]

    which implies

    \[δρ = (1-χ_0 K)^{-1} χ_0 δV_{\rm ext}.\]

    From this we identify the polarizability operator to be $χ = (1-χ_0 K)^{-1} χ_0$. Numerically, we apply $χ$ to $δV = -x$ by solving a linear equation (the Dyson equation) iteratively.

    using KrylovKit
     
     # Apply ``(1- χ_0 K)``
     function dielectric_operator(δρ)
    @@ -54,20 +54,21 @@
     
     println("Non-interacting polarizability: $(dipole(basis, δρ_nointeract))")
     println("Interacting polarizability:     $(dipole(basis, δρ))")
    WARNING: using KrylovKit.basis in module Main conflicts with an existing identifier.
    -[ Info: GMRES linsolve in iter 1; step 1: normres = 2.493920978660e-01
    -[ Info: GMRES linsolve in iter 1; step 2: normres = 3.766551922672e-03
    -[ Info: GMRES linsolve in iter 1; step 3: normres = 2.852732279044e-04
    -[ Info: GMRES linsolve in iter 1; step 4: normres = 4.694605042428e-06
    -[ Info: GMRES linsolve in iter 1; step 5: normres = 1.088781781500e-08
    -[ Info: GMRES linsolve in iter 1; step 6: normres = 6.278994676847e-11
    -[ Info: GMRES linsolve in iter 1; step 7: normres = 8.672442999052e-13
    -[ Info: GMRES linsolve in iter 1; finished at step 7: normres = 8.672442999052e-13
    -[ Info: GMRES linsolve in iter 2; step 1: normres = 9.489798853238e-10
    -[ Info: GMRES linsolve in iter 2; step 2: normres = 5.265419885015e-11
    -[ Info: GMRES linsolve in iter 2; step 3: normres = 9.155119484875e-13
    -[ Info: GMRES linsolve in iter 2; finished at step 3: normres = 9.155119484875e-13
    -┌ Info: GMRES linsolve converged at iteration 2, step 3:
    -*  norm of residual = 9.154930218192818e-13
    -*  number of operations = 12
    -Non-interacting polarizability: 1.9257125417947
    -Interacting polarizability:     1.773654874585549

    As expected, the interacting polarizability matches the finite difference result. The non-interacting polarizability is higher.

    +[ Info: GMRES linsolve in iter 1; step 1: normres = 2.493920654129e-01 +[ Info: GMRES linsolve in iter 1; step 2: normres = 3.766551195940e-03 +[ Info: GMRES linsolve in iter 1; step 3: normres = 2.852766050656e-04 +[ Info: GMRES linsolve in iter 1; step 4: normres = 4.694593858883e-06 +[ Info: GMRES linsolve in iter 1; step 5: normres = 1.088787551185e-08 +[ Info: GMRES linsolve in iter 1; step 6: normres = 6.274542361282e-11 +[ Info: GMRES linsolve in iter 1; step 7: normres = 6.978491677121e-13 +[ Info: GMRES linsolve in iter 1; finished at step 7: normres = 6.978491677121e-13 +[ Info: GMRES linsolve in iter 2; step 1: normres = 1.632685441407e-09 +[ Info: GMRES linsolve in iter 2; step 2: normres = 3.217878884262e-11 +[ Info: GMRES linsolve in iter 2; step 3: normres = 4.775606916668e-12 +[ Info: GMRES linsolve in iter 2; step 4: normres = 6.131875873247e-14 +[ Info: GMRES linsolve in iter 2; finished at step 4: normres = 6.131875873247e-14 +┌ Info: GMRES linsolve converged at iteration 2, step 4: +* norm of residual = 6.104688792625191e-14 +* number of operations = 13 +Non-interacting polarizability: 1.9257125361654672 +Interacting polarizability: 1.7736548588989334

    As expected, the interacting polarizability matches the finite difference result. The non-interacting polarizability is higher.

    diff --git a/dev/examples/pseudopotentials.ipynb b/dev/examples/pseudopotentials.ipynb index c58fa3e11f..2b36a18442 100644 --- a/dev/examples/pseudopotentials.ipynb +++ b/dev/examples/pseudopotentials.ipynb @@ -178,20 +178,20 @@ "text": [ "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -7.920990227950 -0.69 5.8 \n", - " 2 -7.925543433935 -2.34 -1.22 1.8 225ms\n", - " 3 -7.926171749459 -3.20 -2.43 2.5 260ms\n", - " 4 -7.926189704345 -4.75 -3.02 4.2 325ms\n", - " 5 -7.926189831578 -6.90 -4.17 2.0 235ms\n", + " 1 -7.920963644339 -0.69 5.8 \n", + " 2 -7.925542324000 -2.34 -1.22 1.8 432ms\n", + " 3 -7.926171645453 -3.20 -2.43 2.9 484ms\n", + " 4 -7.926189669262 -4.74 -3.03 4.1 576ms\n", + " 5 -7.926189833000 -6.79 -4.20 2.2 476ms\n", "Computing bands along kpath:\n", " Γ -> X -> U and K -> Γ -> L -> W -> X\n", - "\rDiagonalising Hamiltonian kblocks: 2%|▎ | ETA: 0:00:17\u001b[K\rDiagonalising Hamiltonian kblocks: 4%|▋ | ETA: 0:00:13\u001b[K\rDiagonalising Hamiltonian kblocks: 6%|▉ | ETA: 0:00:11\u001b[K\rDiagonalising Hamiltonian kblocks: 7%|█▏ | ETA: 0:00:10\u001b[K\rDiagonalising Hamiltonian kblocks: 9%|█▌ | ETA: 0:00:10\u001b[K\rDiagonalising Hamiltonian kblocks: 11%|█▊ | ETA: 0:00:09\u001b[K\rDiagonalising Hamiltonian kblocks: 13%|██ | ETA: 0:00:09\u001b[K\rDiagonalising Hamiltonian kblocks: 15%|██▍ | ETA: 0:00:08\u001b[K\rDiagonalising Hamiltonian kblocks: 17%|██▋ | ETA: 0:00:08\u001b[K\rDiagonalising Hamiltonian kblocks: 18%|██▉ | ETA: 0:00:08\u001b[K\rDiagonalising Hamiltonian kblocks: 20%|███▎ | ETA: 0:00:07\u001b[K\rDiagonalising Hamiltonian kblocks: 22%|███▌ | ETA: 0:00:07\u001b[K\rDiagonalising Hamiltonian kblocks: 24%|███▉ | ETA: 0:00:07\u001b[K\rDiagonalising Hamiltonian kblocks: 26%|████▏ | ETA: 0:00:07\u001b[K\rDiagonalising Hamiltonian kblocks: 28%|████▍ | ETA: 0:00:07\u001b[K\rDiagonalising Hamiltonian kblocks: 29%|████▊ | ETA: 0:00:06\u001b[K\rDiagonalising Hamiltonian kblocks: 31%|█████ | ETA: 0:00:06\u001b[K\rDiagonalising Hamiltonian kblocks: 33%|█████▎ | ETA: 0:00:06\u001b[K\rDiagonalising Hamiltonian kblocks: 35%|█████▋ | ETA: 0:00:06\u001b[K\rDiagonalising Hamiltonian kblocks: 37%|█████▉ | ETA: 0:00:06\u001b[K\rDiagonalising Hamiltonian kblocks: 39%|██████▏ | ETA: 0:00:06\u001b[K\rDiagonalising Hamiltonian kblocks: 40%|██████▌ | ETA: 0:00:05\u001b[K\rDiagonalising Hamiltonian kblocks: 42%|██████▊ | ETA: 0:00:05\u001b[K\rDiagonalising Hamiltonian kblocks: 44%|███████ | ETA: 0:00:05\u001b[K\rDiagonalising Hamiltonian kblocks: 46%|███████▍ | ETA: 0:00:05\u001b[K\rDiagonalising Hamiltonian kblocks: 48%|███████▋ | ETA: 0:00:05\u001b[K\rDiagonalising Hamiltonian kblocks: 50%|███████▉ | ETA: 0:00:05\u001b[K\rDiagonalising Hamiltonian kblocks: 51%|████████▎ | ETA: 0:00:04\u001b[K\rDiagonalising Hamiltonian kblocks: 53%|████████▌ | ETA: 0:00:04\u001b[K\rDiagonalising Hamiltonian kblocks: 55%|████████▊ | ETA: 0:00:04\u001b[K\rDiagonalising Hamiltonian kblocks: 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ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 83%|█████████████▎ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 84%|█████████████▌ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 86%|█████████████▊ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 88%|██████████████▏ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 90%|██████████████▍ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 92%|██████████████▋ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 94%|███████████████ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 95%|███████████████▎| ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 97%|███████████████▌| ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 99%|███████████████▉| ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 100%|████████████████| Time: 0:00:08\u001b[K\n" + "\rDiagonalising Hamiltonian kblocks: 2%|▎ | ETA: 0:00:30\u001b[K\rDiagonalising Hamiltonian kblocks: 3%|▌ | ETA: 0:00:25\u001b[K\rDiagonalising Hamiltonian kblocks: 4%|▋ | ETA: 0:00:22\u001b[K\rDiagonalising Hamiltonian kblocks: 5%|▊ | ETA: 0:00:20\u001b[K\rDiagonalising Hamiltonian kblocks: 6%|▉ | ETA: 0:00:19\u001b[K\rDiagonalising Hamiltonian kblocks: 6%|█ | ETA: 0:00:18\u001b[K\rDiagonalising Hamiltonian kblocks: 7%|█▏ | ETA: 0:00:18\u001b[K\rDiagonalising Hamiltonian kblocks: 8%|█▍ | ETA: 0:00:17\u001b[K\rDiagonalising Hamiltonian kblocks: 9%|█▌ | ETA: 0:00:17\u001b[K\rDiagonalising Hamiltonian kblocks: 10%|█▋ | ETA: 0:00:16\u001b[K\rDiagonalising Hamiltonian kblocks: 11%|█▊ | ETA: 0:00:16\u001b[K\rDiagonalising Hamiltonian kblocks: 12%|█▉ | ETA: 0:00:15\u001b[K\rDiagonalising Hamiltonian kblocks: 13%|██ | ETA: 0:00:15\u001b[K\rDiagonalising Hamiltonian kblocks: 14%|██▎ | ETA: 0:00:15\u001b[K\rDiagonalising Hamiltonian kblocks: 15%|██▍ | ETA: 0:00:14\u001b[K\rDiagonalising Hamiltonian kblocks: 16%|██▌ | ETA: 0:00:14\u001b[K\rDiagonalising Hamiltonian kblocks: 17%|██▋ | ETA: 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kblocks: 30%|████▉ | ETA: 0:00:11\u001b[K\rDiagonalising Hamiltonian kblocks: 31%|█████ | ETA: 0:00:11\u001b[K\rDiagonalising Hamiltonian kblocks: 32%|█████▏ | ETA: 0:00:11\u001b[K\rDiagonalising Hamiltonian kblocks: 33%|█████▎ | ETA: 0:00:11\u001b[K\rDiagonalising Hamiltonian kblocks: 34%|█████▍ | ETA: 0:00:11\u001b[K\rDiagonalising Hamiltonian kblocks: 35%|█████▋ | ETA: 0:00:10\u001b[K\rDiagonalising Hamiltonian kblocks: 36%|█████▊ | ETA: 0:00:10\u001b[K\rDiagonalising Hamiltonian kblocks: 37%|█████▉ | ETA: 0:00:10\u001b[K\rDiagonalising Hamiltonian kblocks: 38%|██████ | ETA: 0:00:10\u001b[K\rDiagonalising Hamiltonian kblocks: 39%|██████▏ | ETA: 0:00:10\u001b[K\rDiagonalising Hamiltonian kblocks: 39%|██████▎ | ETA: 0:00:10\u001b[K\rDiagonalising Hamiltonian kblocks: 40%|██████▌ | ETA: 0:00:09\u001b[K\rDiagonalising Hamiltonian kblocks: 41%|██████▋ | ETA: 0:00:09\u001b[K\rDiagonalising Hamiltonian kblocks: 42%|██████▊ | ETA: 0:00:09\u001b[K\rDiagonalising Hamiltonian kblocks: 43%|██████▉ | ETA: 0:00:09\u001b[K\rDiagonalising Hamiltonian kblocks: 44%|███████ | ETA: 0:00:09\u001b[K\rDiagonalising Hamiltonian kblocks: 45%|███████▎ | ETA: 0:00:09\u001b[K\rDiagonalising Hamiltonian kblocks: 46%|███████▍ | ETA: 0:00:09\u001b[K\rDiagonalising Hamiltonian kblocks: 47%|███████▌ | ETA: 0:00:08\u001b[K\rDiagonalising Hamiltonian kblocks: 48%|███████▋ | ETA: 0:00:08\u001b[K\rDiagonalising Hamiltonian kblocks: 49%|███████▊ | ETA: 0:00:08\u001b[K\rDiagonalising Hamiltonian kblocks: 50%|███████▉ | ETA: 0:00:08\u001b[K\rDiagonalising Hamiltonian kblocks: 50%|████████▏ | ETA: 0:00:08\u001b[K\rDiagonalising Hamiltonian kblocks: 51%|████████▎ | ETA: 0:00:08\u001b[K\rDiagonalising Hamiltonian kblocks: 52%|████████▍ | ETA: 0:00:08\u001b[K\rDiagonalising Hamiltonian kblocks: 53%|████████▌ | ETA: 0:00:07\u001b[K\rDiagonalising Hamiltonian kblocks: 54%|████████▋ | ETA: 0:00:07\u001b[K\rDiagonalising Hamiltonian kblocks: 55%|████████▊ | ETA: 0:00:07\u001b[K\rDiagonalising Hamiltonian kblocks: 56%|█████████ | ETA: 0:00:07\u001b[K\rDiagonalising Hamiltonian kblocks: 57%|█████████▏ | ETA: 0:00:07\u001b[K\rDiagonalising Hamiltonian kblocks: 58%|█████████▎ | ETA: 0:00:07\u001b[K\rDiagonalising Hamiltonian kblocks: 59%|█████████▍ | ETA: 0:00:07\u001b[K\rDiagonalising Hamiltonian kblocks: 60%|█████████▌ | ETA: 0:00:06\u001b[K\rDiagonalising Hamiltonian kblocks: 61%|█████████▊ | ETA: 0:00:06\u001b[K\rDiagonalising Hamiltonian kblocks: 61%|█████████▉ | ETA: 0:00:06\u001b[K\rDiagonalising Hamiltonian kblocks: 62%|██████████ | ETA: 0:00:06\u001b[K\rDiagonalising Hamiltonian kblocks: 63%|██████████▏ | ETA: 0:00:06\u001b[K\rDiagonalising Hamiltonian kblocks: 64%|██████████▎ | ETA: 0:00:06\u001b[K\rDiagonalising Hamiltonian kblocks: 65%|██████████▍ | ETA: 0:00:05\u001b[K\rDiagonalising Hamiltonian kblocks: 66%|██████████▋ | ETA: 0:00:05\u001b[K\rDiagonalising Hamiltonian kblocks: 67%|██████████▊ | ETA: 0:00:05\u001b[K\rDiagonalising Hamiltonian kblocks: 68%|██████████▉ | ETA: 0:00:05\u001b[K\rDiagonalising Hamiltonian kblocks: 69%|███████████ | ETA: 0:00:05\u001b[K\rDiagonalising Hamiltonian kblocks: 70%|███████████▏ | ETA: 0:00:05\u001b[K\rDiagonalising Hamiltonian kblocks: 71%|███████████▎ | ETA: 0:00:05\u001b[K\rDiagonalising Hamiltonian kblocks: 72%|███████████▌ | ETA: 0:00:04\u001b[K\rDiagonalising Hamiltonian kblocks: 72%|███████████▋ | ETA: 0:00:04\u001b[K\rDiagonalising Hamiltonian kblocks: 73%|███████████▊ | ETA: 0:00:04\u001b[K\rDiagonalising Hamiltonian kblocks: 74%|███████████▉ | ETA: 0:00:04\u001b[K\rDiagonalising Hamiltonian kblocks: 75%|████████████ | ETA: 0:00:04\u001b[K\rDiagonalising Hamiltonian kblocks: 76%|████████████▏ | ETA: 0:00:04\u001b[K\rDiagonalising Hamiltonian kblocks: 77%|████████████▍ | ETA: 0:00:04\u001b[K\rDiagonalising Hamiltonian kblocks: 78%|████████████▌ | ETA: 0:00:03\u001b[K\rDiagonalising Hamiltonian kblocks: 79%|████████████▋ | ETA: 0:00:03\u001b[K\rDiagonalising Hamiltonian kblocks: 80%|████████████▊ | ETA: 0:00:03\u001b[K\rDiagonalising Hamiltonian kblocks: 81%|████████████▉ | ETA: 0:00:03\u001b[K\rDiagonalising Hamiltonian kblocks: 82%|█████████████▏ | ETA: 0:00:03\u001b[K\rDiagonalising Hamiltonian kblocks: 83%|█████████████▎ | ETA: 0:00:03\u001b[K\rDiagonalising Hamiltonian kblocks: 83%|█████████████▍ | ETA: 0:00:03\u001b[K\rDiagonalising Hamiltonian kblocks: 84%|█████████████▌ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 85%|█████████████▋ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 86%|█████████████▊ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 87%|██████████████ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 88%|██████████████▏ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 89%|██████████████▎ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 90%|██████████████▍ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 91%|██████████████▌ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 92%|██████████████▋ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 93%|██████████████▉ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 94%|███████████████ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 94%|███████████████▏| ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 95%|███████████████▎| ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 96%|███████████████▍| ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 97%|███████████████▌| ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 98%|███████████████▊| ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 99%|███████████████▉| ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 100%|████████████████| Time: 0:00:15\u001b[K\n" ] }, { "output_type": "execute_result", "data": { - "text/plain": "Energy breakdown (in Ha):\n Kinetic 3.1590192 \n AtomicLocal -2.1424863\n AtomicNonlocal 1.6043206 \n Ewald -8.4004648\n PspCorrection -0.2948928\n Hartree 0.5515703 \n Xc -2.4000939\n Entropy -0.0031622\n\n total -7.926189831578" + "text/plain": "Energy breakdown (in Ha):\n Kinetic 3.1590151 \n AtomicLocal -2.1424801\n AtomicNonlocal 1.6043206 \n Ewald -8.4004648\n PspCorrection -0.2948928\n Hartree 0.5515667 \n Xc -2.4000924\n Entropy -0.0031622\n\n total -7.926189833000" }, "metadata": {}, "execution_count": 4 @@ -213,21 +213,21 @@ "text": [ "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -8.515342295200 -0.93 6.0 \n", - " 2 -8.518464706604 -2.51 -1.44 1.8 216ms\n", - " 3 -8.518846200661 -3.42 -2.78 3.2 312ms\n", - " 4 -8.518860713973 -4.84 -3.19 4.6 386ms\n", - " 5 -8.518860781232 -7.17 -3.51 2.0 231ms\n", - " 6 -8.518860826769 -7.34 -4.82 1.9 238ms\n", + " 1 -8.515332324006 -0.93 6.2 \n", + " 2 -8.518461662559 -2.50 -1.44 1.4 406ms\n", + " 3 -8.518844668890 -3.42 -2.77 3.2 525ms\n", + " 4 -8.518860694563 -4.80 -3.17 5.0 693ms\n", + " 5 -8.518860768680 -7.13 -3.45 2.0 447ms\n", + " 6 -8.518860826238 -7.24 -4.83 1.6 432ms\n", "Computing bands along kpath:\n", " Γ -> X -> U and K -> Γ -> L -> W -> X\n", - "\rDiagonalising Hamiltonian kblocks: 2%|▎ | ETA: 0:00:19\u001b[K\rDiagonalising Hamiltonian kblocks: 4%|▋ | ETA: 0:00:14\u001b[K\rDiagonalising Hamiltonian kblocks: 6%|▉ | ETA: 0:00:12\u001b[K\rDiagonalising Hamiltonian kblocks: 6%|█ | ETA: 0:00:12\u001b[K\rDiagonalising Hamiltonian kblocks: 8%|█▍ | ETA: 0:00:11\u001b[K\rDiagonalising Hamiltonian kblocks: 10%|█▋ | ETA: 0:00:10\u001b[K\rDiagonalising Hamiltonian kblocks: 12%|█▉ | ETA: 0:00:10\u001b[K\rDiagonalising Hamiltonian kblocks: 14%|██▎ | ETA: 0:00:09\u001b[K\rDiagonalising Hamiltonian kblocks: 16%|██▌ | ETA: 0:00:09\u001b[K\rDiagonalising Hamiltonian kblocks: 17%|██▊ | ETA: 0:00:09\u001b[K\rDiagonalising Hamiltonian kblocks: 19%|███▏ | ETA: 0:00:08\u001b[K\rDiagonalising Hamiltonian kblocks: 21%|███▍ | ETA: 0:00:08\u001b[K\rDiagonalising Hamiltonian kblocks: 23%|███▋ | ETA: 0:00:08\u001b[K\rDiagonalising Hamiltonian kblocks: 25%|████ | ETA: 0:00:08\u001b[K\rDiagonalising Hamiltonian kblocks: 27%|████▎ | ETA: 0:00:07\u001b[K\rDiagonalising Hamiltonian kblocks: 28%|████▌ | ETA: 0:00:07\u001b[K\rDiagonalising Hamiltonian kblocks: 30%|████▉ | ETA: 0:00:07\u001b[K\rDiagonalising Hamiltonian kblocks: 31%|█████ | ETA: 0:00:07\u001b[K\rDiagonalising Hamiltonian kblocks: 33%|█████▎ | ETA: 0:00:07\u001b[K\rDiagonalising Hamiltonian kblocks: 35%|█████▋ | ETA: 0:00:06\u001b[K\rDiagonalising Hamiltonian kblocks: 37%|█████▉ | ETA: 0:00:06\u001b[K\rDiagonalising Hamiltonian kblocks: 39%|██████▏ | ETA: 0:00:06\u001b[K\rDiagonalising Hamiltonian kblocks: 40%|██████▌ | ETA: 0:00:06\u001b[K\rDiagonalising Hamiltonian kblocks: 42%|██████▊ | ETA: 0:00:06\u001b[K\rDiagonalising Hamiltonian kblocks: 44%|███████ | ETA: 0:00:05\u001b[K\rDiagonalising Hamiltonian kblocks: 46%|███████▍ | ETA: 0:00:05\u001b[K\rDiagonalising Hamiltonian kblocks: 47%|███████▌ | ETA: 0:00:05\u001b[K\rDiagonalising Hamiltonian kblocks: 49%|███████▊ | ETA: 0:00:05\u001b[K\rDiagonalising Hamiltonian kblocks: 50%|████████▏ | ETA: 0:00:05\u001b[K\rDiagonalising Hamiltonian kblocks: 52%|████████▍ | ETA: 0:00:05\u001b[K\rDiagonalising Hamiltonian kblocks: 54%|████████▋ | ETA: 0:00:04\u001b[K\rDiagonalising Hamiltonian kblocks: 56%|█████████ | ETA: 0:00:04\u001b[K\rDiagonalising Hamiltonian kblocks: 58%|█████████▎ | ETA: 0:00:04\u001b[K\rDiagonalising Hamiltonian kblocks: 60%|█████████▌ | ETA: 0:00:04\u001b[K\rDiagonalising Hamiltonian kblocks: 61%|█████████▉ | ETA: 0:00:04\u001b[K\rDiagonalising Hamiltonian kblocks: 63%|██████████▏ | ETA: 0:00:03\u001b[K\rDiagonalising Hamiltonian kblocks: 65%|██████████▍ | ETA: 0:00:03\u001b[K\rDiagonalising Hamiltonian kblocks: 67%|██████████▊ | ETA: 0:00:03\u001b[K\rDiagonalising Hamiltonian kblocks: 69%|███████████ | ETA: 0:00:03\u001b[K\rDiagonalising Hamiltonian kblocks: 71%|███████████▎ | ETA: 0:00:03\u001b[K\rDiagonalising Hamiltonian kblocks: 72%|███████████▋ | ETA: 0:00:03\u001b[K\rDiagonalising Hamiltonian kblocks: 74%|███████████▉ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 76%|████████████▏ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 78%|████████████▌ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 80%|████████████▊ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 82%|█████████████▏ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 83%|█████████████▍ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 85%|█████████████▋ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 87%|██████████████ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 89%|██████████████▎ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 91%|██████████████▌ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 93%|██████████████▉ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 94%|███████████████▏| ETA: 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| ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 88%|██████████████▏ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 89%|██████████████▎ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 90%|██████████████▍ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 91%|██████████████▌ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 92%|██████████████▋ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 93%|██████████████▉ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 94%|███████████████ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 94%|███████████████▏| ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 95%|███████████████▎| ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 96%|███████████████▍| ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 97%|███████████████▌| ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 98%|███████████████▊| ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 99%|███████████████▉| ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 100%|████████████████| Time: 0:00:16\u001b[K\n" ] }, { "output_type": "execute_result", "data": { - "text/plain": "Energy breakdown (in Ha):\n Kinetic 3.0954193 \n AtomicLocal -2.3650808\n AtomicNonlocal 1.3082674 \n Ewald -8.4004648\n PspCorrection 0.3951970 \n Hartree 0.5521872 \n Xc -3.1011668\n Entropy -0.0032193\n\n total -8.518860826769" + "text/plain": "Energy breakdown (in Ha):\n Kinetic 3.0954263 \n AtomicLocal -2.3650892\n AtomicNonlocal 1.3082666 \n Ewald -8.4004648\n PspCorrection 0.3951970 \n Hartree 0.5521908 \n Xc -3.1011684\n Entropy -0.0032193\n\n total -8.518860826238" }, "metadata": {}, "execution_count": 5 @@ -255,410 +255,410 @@ "output_type": "execute_result", "data": { "text/plain": "Plot{Plots.GRBackend() n=116}", - "image/png": 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", + "image/png": 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"\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" ] }, "metadata": {}, diff --git a/dev/examples/pseudopotentials/0593e3cb.svg b/dev/examples/pseudopotentials/0593e3cb.svg deleted file mode 100644 index 1df607771d..0000000000 --- a/dev/examples/pseudopotentials/0593e3cb.svg +++ /dev/null @@ -1,200 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/dev/examples/pseudopotentials/afd8af54.svg b/dev/examples/pseudopotentials/afd8af54.svg new file mode 100644 index 0000000000..2fbadd8bf6 --- /dev/null +++ b/dev/examples/pseudopotentials/afd8af54.svg @@ -0,0 +1,200 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/examples/pseudopotentials/index.html b/dev/examples/pseudopotentials/index.html index 1f5722c5e3..dfdb89ebf9 100644 --- a/dev/examples/pseudopotentials/index.html +++ b/dev/examples/pseudopotentials/index.html @@ -21,24 +21,24 @@ (; scfres, bandplot) end;

    The SCF and bandstructure calculations can then be performed using the two PSPs, where we notice in particular the difference in total energies.

    result_hgh = run_bands(psp_hgh)
     result_hgh.scfres.energies
    Energy breakdown (in Ha):
    -    Kinetic             3.1590201 
    -    AtomicLocal         -2.1425030
    -    AtomicNonlocal      1.6043331 
    +    Kinetic             3.1590230 
    +    AtomicLocal         -2.1424934
    +    AtomicNonlocal      1.6043220 
         Ewald               -8.4004648
         PspCorrection       -0.2948928
    -    Hartree             0.5515751 
    -    Xc                  -2.4000956
    +    Hartree             0.5515733 
    +    Xc                  -2.4000950
         Entropy             -0.0031621
     
    -    total               -7.926189833099
    result_upf = run_bands(psp_upf)
    +    total               -7.926189831433
    result_upf = run_bands(psp_upf)
     result_upf.scfres.energies
    Energy breakdown (in Ha):
    -    Kinetic             3.0954104 
    -    AtomicLocal         -2.3650712
    -    AtomicNonlocal      1.3082683 
    +    Kinetic             3.0954102 
    +    AtomicLocal         -2.3650693
    +    AtomicNonlocal      1.3082674 
         Ewald               -8.4004648
         PspCorrection       0.3951970 
    -    Hartree             0.5521842 
    -    Xc                  -3.1011655
    -    Entropy             -0.0032192
    +    Hartree             0.5521830 
    +    Xc                  -3.1011650
    +    Entropy             -0.0032193
     
    -    total               -8.518860827263

    But while total energies are not physical and thus allowed to differ, the bands (as an example for a physical quantity) are very similar for both pseudos:

    plot(result_hgh.bandplot, result_upf.bandplot, titles=["HGH" "UPF"], size=(800, 400))
    Example block output + total -8.518860827257

    But while total energies are not physical and thus allowed to differ, the bands (as an example for a physical quantity) are very similar for both pseudos:

    plot(result_hgh.bandplot, result_upf.bandplot, titles=["HGH" "UPF"], size=(800, 400))
    Example block output diff --git a/dev/examples/scf_callbacks.ipynb b/dev/examples/scf_callbacks.ipynb index 7a5a3e9db3..74fbc99e1b 100644 --- a/dev/examples/scf_callbacks.ipynb +++ b/dev/examples/scf_callbacks.ipynb @@ -129,13 +129,13 @@ "text": [ "n Energy log10(ΔE) log10(Δρ) α Diag Δtime\n", "--- --------------- --------- --------- ---- ---- ------\n", - " 1 -7.774350441806 -0.70 0.80 4.8 \n", - " 2 -7.779034351599 -2.33 -1.52 0.80 1.0 18.9ms\n", - " 3 -7.779319202965 -3.55 -2.60 0.80 1.5 19.9ms\n", - " 4 -7.779350301152 -4.51 -2.94 0.80 2.5 22.8ms\n", - " 5 -7.779350731219 -6.37 -3.26 0.80 1.0 18.3ms\n", - " 6 -7.779350850436 -6.92 -4.43 0.80 1.0 18.5ms\n", - " 7 -7.779350856095 -8.25 -5.30 0.80 2.5 22.8ms\n" + " 1 -7.774280247424 -0.70 0.80 4.5 \n", + " 2 -7.779027514535 -2.32 -1.52 0.80 1.0 35.5ms\n", + " 3 -7.779314934850 -3.54 -2.59 0.80 1.2 36.4ms\n", + " 4 -7.779350231344 -4.45 -2.91 0.80 2.5 44.1ms\n", + " 5 -7.779350692702 -6.34 -3.18 0.80 1.0 35.4ms\n", + " 6 -7.779350849661 -6.80 -4.61 0.80 1.0 35.5ms\n", + " 7 -7.779350856126 -8.19 -5.04 0.80 2.8 140ms\n" ] } ], @@ -159,174 +159,174 @@ "output_type": "execute_result", "data": { "text/plain": "Plot{Plots.GRBackend() n=1}", - "image/png": 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", + "image/png": 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b/dev/examples/scf_callbacks/dcc5f478.svg @@ -0,0 +1,82 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/examples/scf_callbacks/index.html b/dev/examples/scf_callbacks/index.html index d58ee43ab5..f142e7ce8d 100644 --- a/dev/examples/scf_callbacks/index.html +++ b/dev/examples/scf_callbacks/index.html @@ -18,10 +18,10 @@ end callback = DFTK.ScfDefaultCallback() ∘ plot_callback;

    Notice that for constructing the callback function we chained the plot_callback (which does the plotting) with the ScfDefaultCallback, such that when using the plot_callback function with self_consistent_field we still get the usual convergence table printed. We run the SCF with this callback …

    scfres = self_consistent_field(basis; tol=1e-5, callback);
    n     Energy            log10(ΔE)   log10(Δρ)   α      Diag   Δtime
     ---   ---------------   ---------   ---------   ----   ----   ------
    -  1   -7.774343543253                   -0.70   0.80    4.8
    -  2   -7.779039917229       -2.33       -1.52   0.80    1.0   18.5ms
    -  3   -7.779317680594       -3.56       -2.60   0.80    1.2   18.2ms
    -  4   -7.779350273236       -4.49       -2.92   0.80    2.5   22.0ms
    -  5   -7.779350707422       -6.36       -3.20   0.80    1.0   17.8ms
    -  6   -7.779350849731       -6.85       -4.49   0.80    1.0   17.9ms
    -  7   -7.779350856100       -8.20       -5.13   0.80    2.5   22.2ms

    … and show the plot

    p
    Example block output

    The info object passed to the callback contains not just the densities but also the complete Bloch wave (in ψ), the occupation, band eigenvalues and so on. See src/scf/self_consistent_field.jl for all currently available keys.

    Debugging with callbacks

    Very handy for debugging SCF algorithms is to employ callbacks with an @infiltrate from Infiltrator.jl to interactively monitor what is happening each SCF step.

    + 1 -7.774300308622 -0.70 0.80 4.8 + 2 -7.779035160829 -2.32 -1.52 0.80 1.0 36.1ms + 3 -7.779318847297 -3.55 -2.59 0.80 1.5 37.4ms + 4 -7.779350318791 -4.50 -2.93 0.80 2.8 46.6ms + 5 -7.779350727172 -6.39 -3.24 0.80 1.0 111ms + 6 -7.779350851866 -6.90 -4.49 0.80 1.0 36.2ms + 7 -7.779350856107 -8.37 -5.27 0.80 2.5 46.6ms

    … and show the plot

    p
    Example block output

    The info object passed to the callback contains not just the densities but also the complete Bloch wave (in ψ), the occupation, band eigenvalues and so on. See src/scf/self_consistent_field.jl for all currently available keys.

    Debugging with callbacks

    Very handy for debugging SCF algorithms is to employ callbacks with an @infiltrate from Infiltrator.jl to interactively monitor what is happening each SCF step.

    diff --git a/dev/examples/supercells.ipynb b/dev/examples/supercells.ipynb index 867f50d469..fbca9e378a 100644 --- a/dev/examples/supercells.ipynb +++ b/dev/examples/supercells.ipynb @@ -161,17 +161,17 @@ "└ @ ASEconvert ~/.julia/packages/ASEconvert/CNQ1A/src/ASEconvert.jl:123\n", "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -8.298531626219 -0.85 5.1 \n", - " 2 -8.300206365735 -2.78 -1.26 1.0 80.9ms\n", - " 3 -8.300434616577 -3.64 -1.89 3.2 110ms\n", - " 4 -8.300461345170 -4.57 -2.76 1.4 192ms\n", - " 5 -8.300464102091 -5.56 -3.09 4.2 122ms\n", - " 6 -8.300464378742 -6.56 -3.29 4.2 122ms\n", - " 7 -8.300464511836 -6.88 -3.44 2.4 110ms\n", - " 8 -8.300464582838 -7.15 -3.59 1.2 97.5ms\n", - " 9 -8.300464625662 -7.37 -3.78 1.5 122ms\n", - " 10 -8.300464637754 -7.92 -3.95 1.2 83.7ms\n", - " 11 -8.300464643720 -8.22 -4.31 2.1 110ms\n" + " 1 -8.298621972621 -0.85 5.0 \n", + " 2 -8.300231952805 -2.79 -1.25 1.0 177ms\n", + " 3 -8.300440039160 -3.68 -1.89 2.0 204ms\n", + " 4 -8.300461341401 -4.67 -2.75 3.0 417ms\n", + " 5 -8.300463953155 -5.58 -3.07 2.8 233ms\n", + " 6 -8.300464294706 -6.47 -3.26 9.2 351ms\n", + " 7 -8.300464471968 -6.75 -3.41 1.8 203ms\n", + " 8 -8.300464568651 -7.01 -3.56 2.1 230ms\n", + " 9 -8.300464626612 -7.24 -3.77 1.5 253ms\n", + " 10 -8.300464637281 -7.97 -3.93 1.4 178ms\n", + " 11 -8.300464643726 -8.19 -4.28 1.9 194ms\n" ] } ], @@ -192,14 +192,14 @@ "└ @ ASEconvert ~/.julia/packages/ASEconvert/CNQ1A/src/ASEconvert.jl:123\n", "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -16.67425943451 -0.70 6.0 \n", - " 2 -16.67866394036 -2.36 -1.14 1.6 199ms\n", - " 3 -16.67921916287 -3.26 -1.86 2.1 217ms\n", - " 4 -16.67927851760 -4.23 -2.75 3.1 236ms\n", - " 5 -16.67928600560 -5.13 -3.17 4.9 297ms\n", - " 6 -16.67928620175 -6.71 -3.46 2.4 223ms\n", - " 7 -16.67928621803 -7.79 -3.95 1.5 186ms\n", - " 8 -16.67928622142 -8.47 -4.56 2.6 224ms\n" + " 1 -16.67394561144 -0.70 6.2 \n", + " 2 -16.67796519227 -2.40 -1.14 1.8 457ms\n", + " 3 -16.67914304701 -2.93 -1.86 3.4 551ms\n", + " 4 -16.67924735738 -3.98 -2.65 3.2 557ms\n", + " 5 -16.67928460329 -4.43 -3.08 3.6 612ms\n", + " 6 -16.67928619559 -5.80 -3.47 3.8 549ms\n", + " 7 -16.67928621108 -7.81 -3.98 2.5 506ms\n", + " 8 -16.67928622133 -7.99 -4.59 2.5 490ms\n" ] } ], @@ -220,14 +220,34 @@ "└ @ ASEconvert ~/.julia/packages/ASEconvert/CNQ1A/src/ASEconvert.jl:123\n", "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -33.32728163901 -0.56 7.8 \n", - " 2 -33.33457992001 -2.14 -1.00 1.4 699ms\n", - " 3 -33.33599389722 -2.85 -1.72 5.1 930ms\n", - " 4 -33.33618372965 -3.72 -2.63 5.0 904ms\n", - " 5 -33.33693029979 -3.13 -2.70 8.4 1.14s\n", - " 6 -33.33693994653 -5.02 -3.02 1.9 654ms\n", - " 7 -33.33694371562 -5.42 -3.92 4.0 891ms\n", - " 8 -33.33694377559 -7.22 -4.29 5.1 1.05s\n" + " 1 -33.32528650909 -0.56 6.4 \n", + " 2 -33.33256945523 -2.14 -1.00 1.0 1.52s\n", + " 3 -33.33405669182 -2.83 -1.75 4.1 1.95s\n", + " 4 -33.33426400863 -3.68 -2.64 4.2 1.88s\n", + " 5 -33.33610791309 -2.73 -2.44 4.5 2.20s\n", + " 6 -33.33694242377 -3.08 -2.52 7.9 2.17s\n", + " 7 -33.33694232426 + -7.00 -2.52 1.9 1.50s\n", + " 8 -33.33676145360 + -3.74 -2.21 2.8 2.06s\n", + " 9 -33.33669696443 + -4.19 -2.14 1.0 1.47s\n", + " 10 -33.33670326362 -5.20 -2.15 1.0 1.48s\n", + " 11 -33.33671699001 -4.86 -2.16 1.0 1.49s\n", + " 12 -33.33674014325 -4.64 -2.18 1.1 1.51s\n", + " 13 -33.33676020425 -4.70 -2.21 1.8 1.58s\n", + " 14 -33.33677074018 -4.98 -2.22 1.0 1.41s\n", + " 15 -33.33679011958 -4.71 -2.25 1.0 1.40s\n", + " 16 -33.33679585711 -5.24 -2.26 1.0 1.44s\n", + " 17 -33.33694326708 -3.83 -3.64 1.6 1.69s\n", + " 18 -33.33694360072 -6.48 -3.63 4.8 2.52s\n", + " 19 -33.33694364807 -7.32 -3.63 1.0 1.38s\n", + " 20 -33.33694367732 -7.53 -3.68 1.0 1.36s\n", + " 21 -33.33694372781 -7.30 -3.77 1.0 1.36s\n", + " 22 -33.33694376340 -7.45 -3.99 1.0 1.37s\n", + " 23 -33.33694374219 + -7.67 -3.96 1.6 1.45s\n", + " 24 -33.33694373292 + -8.03 -3.97 1.0 1.36s\n", + " 25 -33.33694373430 -8.86 -3.96 1.0 1.36s\n", + " 26 -33.33694372927 + -8.30 -3.93 1.0 1.40s\n", + " 27 -33.33694373435 -8.29 -3.95 1.0 1.36s\n", + " 28 -33.33694375051 -7.79 -4.03 1.0 1.38s\n" ] } ], @@ -257,13 +277,13 @@ "└ @ ASEconvert ~/.julia/packages/ASEconvert/CNQ1A/src/ASEconvert.jl:123\n", "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -8.298485169977 -0.85 5.1 \n", - " 2 -8.300255951151 -2.75 -1.58 1.0 61.3ms\n", - " 3 -8.300345968852 -4.05 -2.24 3.0 89.5ms\n", - " 4 -8.300320111140 + -4.59 -2.17 2.0 82.9ms\n", - " 5 -8.300463868665 -3.84 -3.48 1.0 61.2ms\n", - " 6 -8.300464481168 -6.21 -3.64 2.8 104ms\n", - " 7 -8.300464623951 -6.85 -4.05 1.1 64.1ms\n" + " 1 -8.298268771986 -0.85 5.1 \n", + " 2 -8.300238496191 -2.71 -1.59 1.0 164ms\n", + " 3 -8.300422672664 -3.73 -2.57 3.2 202ms\n", + " 4 -8.300383945124 + -4.41 -2.30 3.8 242ms\n", + " 5 -8.300464162874 -4.10 -3.39 1.0 140ms\n", + " 6 -8.300464557823 -6.40 -3.74 4.0 254ms\n", + " 7 -8.300464638778 -7.09 -4.24 3.9 253ms\n" ] } ], @@ -284,22 +304,20 @@ "└ @ ASEconvert ~/.julia/packages/ASEconvert/CNQ1A/src/ASEconvert.jl:123\n", "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -33.32576352335 -0.56 6.9 \n", - " 2 -33.29339869098 + -1.49 -1.26 1.0 566ms\n", - " 3 +4.346253709385 + 1.58 -0.28 6.9 1.34s\n", - " 4 -33.31496780745 1.58 -1.64 6.8 1.25s\n", - " 5 -33.23824192127 + -1.12 -1.32 3.4 951ms\n", - " 6 -33.04641947119 + -0.72 -1.33 3.2 878ms\n", - " 7 -33.20712674089 -0.79 -1.50 4.1 862ms\n", - " 8 -33.33603918041 -0.89 -2.29 3.2 811ms\n", - " 9 -33.33677937818 -3.13 -2.68 3.8 905ms\n", - " 10 -33.33669526947 + -4.08 -2.58 2.6 837ms\n", - " 11 -33.33691721789 -3.65 -3.04 2.0 698ms\n", - " 12 -33.33693987238 -4.64 -3.26 2.5 700ms\n", - " 13 -33.33694092391 -5.98 -3.49 2.6 757ms\n", - " 14 -33.33694318291 -5.65 -3.98 1.8 656ms\n", - " 15 -33.33694247933 + -6.15 -3.99 3.4 902ms\n", - " 16 -33.33694366195 -5.93 -4.47 2.9 758ms\n" + " 1 -33.32653379053 -0.56 7.0 \n", + " 2 -33.31649073521 + -2.00 -1.27 1.5 1.37s\n", + " 3 -15.42070715287 + 1.25 -0.44 6.4 2.87s\n", + " 4 -33.33032577719 1.25 -1.97 5.2 2.45s\n", + " 5 -33.21569582581 + -0.94 -1.35 4.1 2.43s\n", + " 6 -32.94934970602 + -0.57 -1.27 4.8 2.44s\n", + " 7 -33.32251328838 -0.43 -1.92 4.8 2.21s\n", + " 8 -33.33573190204 -1.88 -2.46 2.8 1.58s\n", + " 9 -33.33661905843 -3.05 -2.50 3.8 2.03s\n", + " 10 -33.33656045232 + -4.23 -2.73 1.9 1.56s\n", + " 11 -33.33688853926 -3.48 -3.10 2.2 1.51s\n", + " 12 -33.33692295458 -4.46 -3.34 4.0 1.96s\n", + " 13 -33.33694288654 -4.70 -3.80 2.8 1.60s\n", + " 14 -33.33694361127 -6.14 -4.09 3.6 2.08s\n" ] } ], diff --git a/dev/examples/supercells/index.html b/dev/examples/supercells/index.html index aa8e4f20c1..4105d79094 100644 --- a/dev/examples/supercells/index.html +++ b/dev/examples/supercells/index.html @@ -32,65 +32,66 @@ @ ASEconvert ~/.julia/packages/ASEconvert/CNQ1A/src/ASEconvert.jl:123 n Energy log10(ΔE) log10(Δρ) Diag Δtime --- --------------- --------- --------- ---- ------ - 1 -8.298414623799 -0.85 5.4 - 2 -8.300179822179 -2.75 -1.25 1.2 80.2ms - 3 -8.300427532513 -3.61 -1.86 2.6 95.3ms - 4 -8.300461637063 -4.47 -2.69 1.9 127ms - 5 -8.300464393643 -5.56 -3.19 2.6 112ms - 6 -8.300464509477 -6.94 -3.32 9.8 174ms - 7 -8.300464563999 -7.26 -3.48 1.5 129ms - 8 -8.300464601388 -7.43 -3.62 2.4 94.5ms - 9 -8.300464633143 -7.50 -3.85 1.5 95.5ms - 10 -8.300464638900 -8.24 -3.97 1.4 96.6ms - 11 -8.300464643657 -8.32 -4.31 3.2 148ms
    self_consistent_field(aluminium_setup(2); tol=1e-4);
    ┌ Warning: Skipping atomic property pseudopotential, which is not supported in ASE.
    +  1   -8.298434611794                   -0.85    5.2
    +  2   -8.300207753497       -2.75       -1.26    1.1    159ms
    +  3   -8.300434865614       -3.64       -1.89    2.4    184ms
    +  4   -8.300461644378       -4.57       -2.77    2.6    189ms
    +  5   -8.300464267742       -5.58       -3.10    4.2    241ms
    +  6   -8.300464446009       -6.75       -3.26    4.6    251ms
    +  7   -8.300464543410       -7.01       -3.40    1.9    199ms
    +  8   -8.300464597597       -7.27       -3.55    1.4    197ms
    +  9   -8.300464629827       -7.49       -3.74    1.2    264ms
    + 10   -8.300464636286       -8.19       -3.85    1.2    170ms
    + 11   -8.300464642583       -8.20       -4.10    1.8    190ms
    self_consistent_field(aluminium_setup(2); tol=1e-4);
    ┌ Warning: Skipping atomic property pseudopotential, which is not supported in ASE.
     @ ASEconvert ~/.julia/packages/ASEconvert/CNQ1A/src/ASEconvert.jl:123
     n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime
     ---   ---------------   ---------   ---------   ----   ------
    -  1   -16.67463002906                   -0.70    6.8
    -  2   -16.67872105910       -2.39       -1.14    1.5    207ms
    -  3   -16.67921089212       -3.31       -1.87    3.2    275ms
    -  4   -16.67927796468       -4.17       -2.76    2.5    264ms
    -  5   -16.67928594666       -5.10       -3.18    5.6    334ms
    -  6   -16.67928620001       -6.60       -3.47    2.2    230ms
    -  7   -16.67928621713       -7.77       -3.94    1.9    205ms
    -  8   -16.67928622146       -8.36       -4.57    2.5    315ms
    self_consistent_field(aluminium_setup(4); tol=1e-4);
    ┌ Warning: Skipping atomic property pseudopotential, which is not supported in ASE.
    +  1   -16.63190552270                   -0.70    5.8
    +  2   -16.63611523317       -2.38       -1.14    1.0    394ms
    +  3   -16.67907996763       -1.37       -1.72    2.6    470ms
    +  4   -16.67927346738       -3.71       -2.22    2.8    584ms
    +  5   -16.67928473683       -4.95       -2.86    3.4    531ms
    +  6   -16.67928617515       -5.84       -3.15    4.6    640ms
    +  7   -16.67928620614       -7.51       -3.29    1.1    371ms
    +  8   -16.67928621920       -7.88       -3.84    1.1    369ms
    +  9   -16.67928622164       -8.61       -4.52    2.4    457ms
    self_consistent_field(aluminium_setup(4); tol=1e-4);
    ┌ Warning: Skipping atomic property pseudopotential, which is not supported in ASE.
     @ ASEconvert ~/.julia/packages/ASEconvert/CNQ1A/src/ASEconvert.jl:123
     n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime
     ---   ---------------   ---------   ---------   ----   ------
    -  1   -33.32460076408                   -0.56    6.1
    -  2   -33.33248059313       -2.10       -1.00    1.0    730ms
    -  3   -33.33408846295       -2.79       -1.74    5.1    926ms
    -  4   -33.33673682007       -2.58       -2.38    2.8    850ms
    -  5   -33.33693705594       -3.70       -2.76   11.5    1.31s
    -  6   -33.33694353044       -5.19       -3.50    2.8    745ms
    -  7   -33.33694372285       -6.72       -3.97    5.1    1.10s
    -  8   -33.33694378145       -7.23       -4.69    3.6    870ms

    When switching off explicitly the LdosMixing, by selecting mixing=SimpleMixing(), the performance of number of required SCF steps starts to increase as we increase the size of the modelled problem:

    self_consistent_field(aluminium_setup(1); tol=1e-4, mixing=SimpleMixing());
    ┌ Warning: Skipping atomic property pseudopotential, which is not supported in ASE.
    +  1   -33.32622807555                   -0.56    7.2
    +  2   -33.33438050707       -2.09       -1.00    1.0    1.54s
    +  3   -33.33590786212       -2.82       -1.73    4.0    1.91s
    +  4   -33.33613525041       -3.64       -2.64    3.1    1.77s
    +  5   -33.33680973060       -3.17       -2.25   11.0    2.98s
    +  6   -33.33684033608       -4.51       -2.31    1.1    1.42s
    +  7   -33.33694302311       -3.99       -3.47    1.6    1.58s
    +  8   -33.33694369837       -6.17       -3.85    4.9    2.50s
    +  9   -33.33694373261       -7.47       -3.94    2.4    1.77s
    + 10   -33.33694378302       -7.30       -5.07    2.9    1.81s

    When switching off explicitly the LdosMixing, by selecting mixing=SimpleMixing(), the performance of number of required SCF steps starts to increase as we increase the size of the modelled problem:

    self_consistent_field(aluminium_setup(1); tol=1e-4, mixing=SimpleMixing());
    ┌ Warning: Skipping atomic property pseudopotential, which is not supported in ASE.
     @ ASEconvert ~/.julia/packages/ASEconvert/CNQ1A/src/ASEconvert.jl:123
     n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime
     ---   ---------------   ---------   ---------   ----   ------
    -  1   -8.298414353554                   -0.85    5.1
    -  2   -8.300253481651       -2.74       -1.59    1.0   63.5ms
    -  3   -8.300403159724       -3.82       -2.42    2.5    130ms
    -  4   -8.300325742152   +   -4.11       -2.18    4.6    133ms
    -  5   -8.300463951127       -3.86       -3.44    1.1   75.8ms
    -  6   -8.300464477319       -6.28       -3.66    5.5    175ms
    -  7   -8.300464634140       -6.80       -4.11    1.0   68.4ms
    self_consistent_field(aluminium_setup(4); tol=1e-4, mixing=SimpleMixing());
    ┌ Warning: Skipping atomic property pseudopotential, which is not supported in ASE.
    +  1   -8.298538258549                   -0.85    5.4
    +  2   -8.300273335769       -2.76       -1.59    1.0    144ms
    +  3   -8.300426434858       -3.82       -2.54    2.5    178ms
    +  4   -8.300374810104   +   -4.29       -2.28    4.1    249ms
    +  5   -8.300464103935       -4.05       -3.41    1.0    135ms
    +  6   -8.300464578822       -6.32       -3.77    2.0    224ms
    +  7   -8.300464639531       -7.22       -4.30    1.2    174ms
    self_consistent_field(aluminium_setup(4); tol=1e-4, mixing=SimpleMixing());
    ┌ Warning: Skipping atomic property pseudopotential, which is not supported in ASE.
     @ ASEconvert ~/.julia/packages/ASEconvert/CNQ1A/src/ASEconvert.jl:123
     n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime
     ---   ---------------   ---------   ---------   ----   ------
    -  1   -33.32458161745                   -0.56    6.2
    -  2   -33.32805832364       -2.46       -1.28    1.0    605ms
    -  3   -25.35453909944   +    0.90       -0.62    5.4    1.12s
    -  4   -33.22520713972        0.90       -1.53    6.2    1.24s
    -  5   -33.27031310675       -1.35       -1.59    3.8    868ms
    -  6   -32.78159822982   +   -0.31       -1.19    4.4    971ms
    -  7   -33.31985994086       -0.27       -1.75    4.2    919ms
    -  8   -33.33438007770       -1.84       -2.26    1.9    716ms
    -  9   -33.33465567250       -3.56       -2.37    2.2    758ms
    - 10   -33.33648062746       -2.74       -2.46    2.8    831ms
    - 11   -33.33678573949       -3.52       -2.83    1.9    718ms
    - 12   -33.33691678685       -3.88       -3.12    3.0    801ms
    - 13   -33.33693246297       -4.80       -3.26    2.5    763ms
    - 14   -33.33694045509       -5.10       -3.51    1.6    683ms
    - 15   -33.33693903320   +   -5.85       -3.60    2.8    840ms
    - 16   -33.33694309558       -5.39       -4.02    1.6    680ms

    For completion let us note that the more traditional mixing=KerkerMixing() approach would also help in this particular setting to obtain a constant number of SCF iterations for an increasing system size (try it!). In contrast to LdosMixing, however, KerkerMixing is only suitable to model bulk metallic system (like the case we are considering here). When modelling metallic surfaces or mixtures of metals and insulators, KerkerMixing fails, while LdosMixing still works well. See the Modelling a gallium arsenide surface example or [HL2021] for details. Due to the general applicability of LdosMixing this method is the default mixing approach in DFTK.

    + 1 -33.32658901908 -0.56 7.2 + 2 -33.28821748728 + -1.42 -1.25 1.4 1.29s + 3 +8.392437840341 + 1.62 -0.25 6.8 3.08s + 4 -33.33015312523 1.62 -1.87 6.0 2.78s + 5 -33.30064199434 + -1.53 -1.48 3.8 2.29s + 6 -33.31624768349 -1.81 -1.74 2.6 1.84s + 7 -33.14391051452 + -0.76 -1.42 4.6 1.99s + 8 -33.32505876932 -0.74 -2.00 4.5 2.01s + 9 -33.33633857755 -1.95 -2.61 2.2 1.45s + 10 -33.33629990808 + -4.41 -2.54 2.8 1.87s + 11 -33.33693182666 -3.20 -3.12 2.4 1.57s + 12 -33.33692932731 + -5.60 -3.31 3.5 1.81s + 13 -33.33694050884 -4.95 -3.46 2.6 1.58s + 14 -33.33694360981 -5.51 -4.05 2.0 1.54s

    For completion let us note that the more traditional mixing=KerkerMixing() approach would also help in this particular setting to obtain a constant number of SCF iterations for an increasing system size (try it!). In contrast to LdosMixing, however, KerkerMixing is only suitable to model bulk metallic system (like the case we are considering here). When modelling metallic surfaces or mixtures of metals and insulators, KerkerMixing fails, while LdosMixing still works well. See the Modelling a gallium arsenide surface example or [HL2021] for details. Due to the general applicability of LdosMixing this method is the default mixing approach in DFTK.

    diff --git a/dev/examples/wannier90.ipynb b/dev/examples/wannier90.ipynb index 300b331a2d..593e247d6e 100644 --- a/dev/examples/wannier90.ipynb +++ b/dev/examples/wannier90.ipynb @@ -29,12 +29,12 @@ "text": [ "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -11.14942808498 -0.67 8.6 \n", - " 2 -11.15007357429 -3.19 -1.40 1.0 208ms\n", - " 3 -11.15010748252 -4.47 -2.77 3.8 308ms\n", - " 4 -11.15010941121 -5.71 -3.31 5.0 432ms\n", - " 5 -11.15010943271 -7.67 -4.19 2.8 305ms\n", - " 6 -11.15010943371 -9.00 -5.06 3.6 350ms\n" + " 1 -11.14940085811 -0.67 8.4 \n", + " 2 -11.15007114167 -3.17 -1.40 1.0 462ms\n", + " 3 -11.15010711601 -4.44 -2.77 3.6 513ms\n", + " 4 -11.15010940477 -5.64 -3.30 4.8 814ms\n", + " 5 -11.15010943226 -7.56 -4.14 2.6 492ms\n", + " 6 -11.15010943372 -8.84 -5.01 4.6 679ms\n" ] } ], @@ -76,991 +76,991 @@ "text": [ "Computing bands along kpath:\n", " Γ -> M -> K -> Γ -> A -> L -> H -> A and L -> M and H -> K\n", - "\rDiagonalising Hamiltonian kblocks: 5%|▊ | ETA: 0:00:16\u001b[K\rDiagonalising Hamiltonian kblocks: 7%|█▏ | ETA: 0:00:14\u001b[K\rDiagonalising Hamiltonian kblocks: 9%|█▌ | ETA: 0:00:12\u001b[K\rDiagonalising Hamiltonian kblocks: 11%|█▉ | ETA: 0:00:11\u001b[K\rDiagonalising Hamiltonian kblocks: 14%|██▏ | ETA: 0:00:10\u001b[K\rDiagonalising Hamiltonian kblocks: 16%|██▌ | ETA: 0:00:10\u001b[K\rDiagonalising Hamiltonian kblocks: 18%|██▉ | ETA: 0:00:09\u001b[K\rDiagonalising Hamiltonian kblocks: 20%|███▎ | ETA: 0:00:09\u001b[K\rDiagonalising Hamiltonian kblocks: 23%|███▋ | ETA: 0:00:09\u001b[K\rDiagonalising Hamiltonian kblocks: 25%|████ | ETA: 0:00:08\u001b[K\rDiagonalising Hamiltonian kblocks: 27%|████▍ | ETA: 0:00:08\u001b[K\rDiagonalising Hamiltonian kblocks: 30%|████▊ | ETA: 0:00:08\u001b[K\rDiagonalising Hamiltonian kblocks: 32%|█████▏ | ETA: 0:00:07\u001b[K\rDiagonalising Hamiltonian kblocks: 34%|█████▌ | ETA: 0:00:07\u001b[K\rDiagonalising Hamiltonian kblocks: 36%|█████▉ | ETA: 0:00:07\u001b[K\rDiagonalising Hamiltonian kblocks: 39%|██████▏ | ETA: 0:00:07\u001b[K\rDiagonalising Hamiltonian kblocks: 41%|██████▌ | ETA: 0:00:06\u001b[K\rDiagonalising Hamiltonian kblocks: 43%|██████▉ | ETA: 0:00:06\u001b[K\rDiagonalising Hamiltonian kblocks: 45%|███████▎ | ETA: 0:00:06\u001b[K\rDiagonalising Hamiltonian kblocks: 48%|███████▋ | ETA: 0:00:06\u001b[K\rDiagonalising Hamiltonian kblocks: 50%|████████ | ETA: 0:00:05\u001b[K\rDiagonalising Hamiltonian kblocks: 52%|████████▍ | ETA: 0:00:05\u001b[K\rDiagonalising Hamiltonian kblocks: 55%|████████▊ | ETA: 0:00:05\u001b[K\rDiagonalising Hamiltonian kblocks: 57%|█████████▏ | ETA: 0:00:05\u001b[K\rDiagonalising Hamiltonian kblocks: 59%|█████████▌ | ETA: 0:00:04\u001b[K\rDiagonalising Hamiltonian kblocks: 61%|█████████▉ | ETA: 0:00:04\u001b[K\rDiagonalising Hamiltonian kblocks: 64%|██████████▏ | ETA: 0:00:04\u001b[K\rDiagonalising Hamiltonian kblocks: 66%|██████████▌ | ETA: 0:00:04\u001b[K\rDiagonalising Hamiltonian kblocks: 68%|██████████▉ | ETA: 0:00:03\u001b[K\rDiagonalising Hamiltonian kblocks: 70%|███████████▎ | ETA: 0:00:03\u001b[K\rDiagonalising Hamiltonian kblocks: 73%|███████████▋ | ETA: 0:00:03\u001b[K\rDiagonalising Hamiltonian kblocks: 75%|████████████ | ETA: 0:00:03\u001b[K\rDiagonalising Hamiltonian kblocks: 77%|████████████▍ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 80%|████████████▊ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 82%|█████████████▏ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 84%|█████████████▌ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 86%|█████████████▉ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 89%|██████████████▏ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 91%|██████████████▌ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 93%|██████████████▉ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 95%|███████████████▎| ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 98%|███████████████▋| ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 100%|████████████████| Time: 0:00:10\u001b[K\n" + "\rDiagonalising Hamiltonian kblocks: 5%|▊ | ETA: 0:00:31\u001b[K\rDiagonalising Hamiltonian kblocks: 7%|█▏ | ETA: 0:00:26\u001b[K\rDiagonalising Hamiltonian kblocks: 9%|█▌ | ETA: 0:00:24\u001b[K\rDiagonalising Hamiltonian kblocks: 11%|█▉ | ETA: 0:00:21\u001b[K\rDiagonalising Hamiltonian kblocks: 14%|██▏ | ETA: 0:00:20\u001b[K\rDiagonalising Hamiltonian kblocks: 16%|██▌ | ETA: 0:00:19\u001b[K\rDiagonalising Hamiltonian kblocks: 18%|██▉ | ETA: 0:00:18\u001b[K\rDiagonalising Hamiltonian kblocks: 20%|███▎ | ETA: 0:00:17\u001b[K\rDiagonalising Hamiltonian kblocks: 23%|███▋ | ETA: 0:00:17\u001b[K\rDiagonalising Hamiltonian kblocks: 25%|████ | ETA: 0:00:16\u001b[K\rDiagonalising Hamiltonian kblocks: 27%|████▍ | ETA: 0:00:15\u001b[K\rDiagonalising Hamiltonian kblocks: 30%|████▊ | ETA: 0:00:15\u001b[K\rDiagonalising Hamiltonian kblocks: 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kblocks: 64%|██████████▏ | ETA: 0:00:07\u001b[K\rDiagonalising Hamiltonian kblocks: 66%|██████████▌ | ETA: 0:00:07\u001b[K\rDiagonalising Hamiltonian kblocks: 68%|██████████▉ | ETA: 0:00:06\u001b[K\rDiagonalising Hamiltonian kblocks: 70%|███████████▎ | ETA: 0:00:06\u001b[K\rDiagonalising Hamiltonian kblocks: 73%|███████████▋ | ETA: 0:00:05\u001b[K\rDiagonalising Hamiltonian kblocks: 75%|████████████ | ETA: 0:00:05\u001b[K\rDiagonalising Hamiltonian kblocks: 77%|████████████▍ | ETA: 0:00:04\u001b[K\rDiagonalising Hamiltonian kblocks: 80%|████████████▊ | ETA: 0:00:04\u001b[K\rDiagonalising Hamiltonian kblocks: 82%|█████████████▏ | ETA: 0:00:04\u001b[K\rDiagonalising Hamiltonian kblocks: 84%|█████████████▌ | ETA: 0:00:03\u001b[K\rDiagonalising Hamiltonian kblocks: 86%|█████████████▉ | ETA: 0:00:03\u001b[K\rDiagonalising Hamiltonian kblocks: 89%|██████████████▏ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 91%|██████████████▌ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 93%|██████████████▉ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 95%|███████████████▎| ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 98%|███████████████▋| ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 100%|████████████████| Time: 0:00:20\u001b[K\n" ] }, { "output_type": "execute_result", "data": { "text/plain": "Plot{Plots.GRBackend() n=123}", - "image/png": 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", + "image/png": 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gMvH6tbTl5VCEAMLCEBYGSLcoBPDoEZjMP7W0QonKiC5X/oIRQ3Q0vLy+HoyRlXv37i1dGnP69OlPnxARATYbMTFfP9Z/+QVOTqji6iBYb3h5wckJcXGoGObyeY0KUCtCcYhShAIePoSRUXuBIwyDEeXisk3MmYQqNG+u/OVFZiZYLJGHz06dQv364jT0+/cwMcGnT99crIFJYeZMIc5jYpBDpMREeHqK1LVDh349QlcZNtub6AkRiJLbtOkDYOtWNGwoRGvWGHPmLGcwYolA9JjN7irmY1moIgRQVobly8FiYdUqkbYBoW/4/HkEBYHJxJAhz8zM3JjMcaamnk2anPbwwN9/g8/Hrl2wtsbAgSLXBxcvom5dyd+IAqKj4eMDKWYYmYmMREREuZH26lUhBWpgzB8+LMPKGPIqwrw81KtXfjJPmkUhgBEjooi2CGzaBgYNpOmFz0dQEEaOlFU64aSkoEULeHp+3X6aNg12dgfq12/p5tbh4sVrcXFo2BC+vkKsFGpFqCrEK0IAdnbeRMVEYDDioqOlPYlz6xZsbKSdEaRn5EhxX9Dt22PrVnHVFy3CiBFVL9bApNCoEa5dk6G8HCLx+XBxwdGjwu9++AAuF9W3FRwcmhN5EE0icmnR4ifBxdGj0b27OPOySpk27RcGI4EIRO+bNGknpqQoRSjg4UP4+kKUQ6mYN/zkCVxcfiZKIgLRC3v7bjweUlLQqhXc3SV4vfftK60x8NIlcLmQwjInG7dv3167douJyaOXL3HggPCjNaiRMR8aKoNdFPIqQgCnTsHSEh8/SrsonD59FlETogSiCAZj9N27UvWSm4smTbBpkxwCCkFgZjAzg8BSmpHxWk/PhyiL6LmmpltgIF/UMFMrQlUhURGuWLGByexoaDjXwsI1Q/zJ2EqEhWHBAmXIV4m0NLDZVW0IFVy+DBsbiDkkw+Ohbl0hG3WqnhTu3IGdnQx2Ucgr0t698PAQ2dHatWjZsurdceN+1tGZTZSkoRFjapor8NUoKYGfH6Ki5BBBCaSnp1taupmaTmCzvQ8ePCKmpHhFCIDPR2ws2GxER1f9JhP/hqdMmauhsZ0IRKktWvSNiACHU747KIbnz6U9RJ+ZCSsrkaZsuUlIOGBq2kZDY7Wurvf58xfbtcNOEVF3VD3mi4tls4tCAUUIYPx4hIQAwPbt8PJ6d+HCBTEPePjwYUPDIKKFREusraM5HGmP7jx8CA5H+ApbPrKyyi2lEyZcMDYeL1ikmpq2+yzaIKNWhKpCoiIEkJqaevDgwU9VTIqiEZyjqr5FryAdO1bd3qtMYCDWrxdX/Y8/4OUl5LqqJ4VZs2Szi0JekQSLQlEBnHg8eHkhPv6biwUFBWPGTHN2DrCxiW7Tht+uXXm0nbdvYWODhAQ5pFACeXl5586dqzh0JQqJilDAs2do2xY+Pt94h4p/wwcOHNLUtCZqz2DYGRjMCA//xsFBFBMnSuUYzOMhIACzZkkuKSu+vr2JnhOB6HKnTmPMzUV+F6p6zCclSYhoUR1FFGF+Pho0wN69OH78lKZms9q1I8zNXZ88eSK0MJ/PHz9+hqWlh4VFJze3x1euwN4ekZFSma8OHoStLZTn2wsAN2+iWbO3DIYl0Q6iVXp6dmIKqxWhqpBGEcrKqlXo31+5TeLPP1Gvnsgf9q1bMDdHQYG4FgIDsWWLkOuqnhRktYtCAZH27RO3KLx2DebmyMoScuvVK3C5aN7861SekgIOByICtf4rkFIRAuDxsGoVWCwsW4ayMvD5/JcinNbz83HpEpo3n0R0hugt0YcGDfyl6UL6Q/SzZqFNG+XvGgAYMGAsg5FEBG3tWDe3X6rHuqxA1WM+JKQ8lJ30KKIIAVy6BDMzuLt3//Ip8MeoURLCOAl2/kaNwocPCAhAp074+FFyR9OmoV07Jf/5zp07r68/nGgBUYyeXpvY2M9pacL3Jg4dOtSunbj9AjGoFaE4VKEInZxw9qwyGywrQ9Om4k4HBgVhxQpxLbx8CRZLeFAJlU4Kqaky20WhgEh8PlxdcUS0QTEsDBMmCL915Ajq1oWd3deFYHw86tYVaYv+7kivCAU8fYo2beDictfMzJ3DCXB09P3w4UNuLs6fx6+/IjQUTk7Q04OHB3x81uvozCDia2ruGjBAqshzy5YhOFhysRMnYGEhzl1eEV6/fqur24XJ9GjZso+xca4Yy6RKx7wcdlEorAgBTJkCNrsf0S0iaGjs+Okn0R8CX8jNhZMTYmNRVobISNjYSP5m5fHQqZNUsRKl582bN2y2D9EHoidGRm79+vHr1UPt2mjVChMmYNs23LuHsjIkJBw0Nq6ro2PaoUP/MtlVsVoRikPpivDMGTRqJPPUL57169Gmjci79++DzZawNzN9ukgvG5VOCrNnSxtzsjKKiLR/P9zdRb7/jx9hZiYy4uKYMQgIAIfz9QTFjz8iIEAlyxfFkVURAuDz0bDhD0Q3icBg7GAy5+rrw8sLo0Zh40bcuFHuw1laWjpixGRra48uXUKk2RGQ8hC9YNl9Qv6YvhLYvbvcILl8uQStrNIxn5Qk7tcqCsUVYVERHBzumpi4GhoG6OsLCb4olEePwGaXRy49dAhcrnC7UWU+foS9vZI3DvbtS3J0bO3p2fn69fIwjFlZOHECS5bghx9Qvz4MDVGrlgfRXqIuder8eFSUX5xo1IpQHEpXhD/8ILNVRDw5ObCwEHcaPSREgmNOcTHMzEQeLFPppNC4sTy764qIxOfD0xNJSSILxMbC11e4piwqgqsrRoxAw4bl55TLytCpEyIi3sXHx1+QI06wKpFDEQLw9v5qPevbN1IppxcqNJAYSkvRsqXI0DlKwdsbR46Az0eDBhIiQ6l0zIeEyJNMSnFFCGDz5osaGu4mJsFaWi8SEzOlrHX8OCwty4/EPHiARo0QHo6SEuTk5GQLfgbVuHnzm+/FGuDzZzCZ7kSHibro68/dK3sUBrUiFIdyFeG7d2AylXwKbdo0DB0q8u7Tp2Ayhe97VbBzJwICRN5V3aSQmgpra3kWxwqKdPCguEUhj4fmzUWeM3n0CFwu+vVDjx7lLaSlvdHSctXSWm5iEjRtmrJdgRVAPkWYnHySzW5mYjJBjD+FrDRvLjlq16RJ6NpVyZaSypw8iQYNeP37jzUz82axppQKvJ5EoLoxX1QEExOZ7aJQkiJs0aIn0WMiMBg3raxk+OMuXIjmzcuPzGdno1cvWFnNZzK92ezmEydGCa2ybVv5idvqeRZVxPz5MUZGrtraNo0atZSjU7UiFIdyFeH8+Uo7dirg1SsJPgjh4ZId8Fq1wv79Iu+qblKYPRs//SRPRcVFatZM3Jbq9eswN68aWKCCTZvQpAl8fLBkCQCsX/+bhsZ6IhDxrKyaKSiYEpFPEQLIzMw8duyYsv7uFy+ifn2Rxy7j4xPq1vW2tGzF5Z5R6W5rp05o2zZRW3stUZm29uzoaNEO1qoc84cOoW1beSoqRRG2atWH6B4RGIx9dep8kj76B5+PH35A6JfEOe/ff9TX9yXiE4HNbisqCVHPnpd1dV3YbF9f3+5FcgeekYWNGzd6e3vLN+zV2ScU5f3797du3SopKRFfjM+nTZsoPFyZXU+dSuPHiwzkn5FBe/fS+PHiWkhLo6dPKTBQmVJJyd69FBT0Hfolopkzac4ckVlEPDyoZ0+aNUv43eHDycWFGjSglSvp1CmysOAaGKQREdHL/Hz9/HwVifyVTZu2tWnzw5Qp8woLC1XRPofDadGiRe3atRVsh8/nP3r0aPHirAkTSEPYNPD27dtJk1Y+f37i9evE0tIJxsY8BXsUxd27lJpKBQW7S0t7EGmWlna/evWeivoST2LidxvzRBQT87O5eXDt2v309aNnzdJcsEDaigwGbdlCd+/S2rVERGVlJYaGBkQMIsrPN8zOLhZa69Gj6UVFR96/v3Djhk98/E4FhX/58uW7d+/ElzE3Nzc2NtbW1lawL1XwP64IExIONGnSrUOH1Y0bt87KyhJaJj8/v0+fMC7XMy9vTOPGRcrq+soVOndOXNqUZcto+HBis8U1snYthYdTzY+ce/coN5eaNavpfgV0705aWpSUJLLAggW0dy9dvSr8bmws/f03jR5NgwaRq2u37t3B4bjb2w9q1y6maVM6f15FUhMRHTp05Kefjpw9u3D1av2xY2eosCfRlJSUBAWFW1p6tm7dW9TcVFRU5OHRoUWL2UeOdNTW3iW0zLNnrwoKXIgMibja2maifj6Ks3QpRURQcHBHHZ0pRH+amMwKCemmor7EUFxMf/xBPXvWfM/luLu7P39++fLlxQYGlwICjB4+pIsXpa2rp0f79tGCBXT2LJmZmbVubctmBzGZP7DZpl272p0+LaRKUVEREZOIiou5jx7lyC02gG7dBnt5jXNy6jtz5mK52/nOyLHY/Pcg0TRar14LohwiaGis79Zt7YoViI3Fxo1ISEBCAo4fR3Iy+vWbqaW1jghaWst+/llpuXxathTnwfX+vWQv7So5eIWiIjNRVJTw8J7SoBSRFiw4p6PjY2HhtmiR8NSomzahWTORNr2rV8HhYPr0quGG9++HuTlmzFBJkEwA4eEziI4RgajYwkJEiDAACphGIekNL1mySld3AREYjMOtWoUlJ2Pv3vIBHxuL2FisX48BA/Zoa88lAlGBlZVn9UaSk9G4caGeXjNNzdhatZa6u4veplaMjIzybfJXr1C79sGRI2cmJ0vwTFXRmD94UE67KJRkGq1g7lwMH45169BVqqjaXxGcbxHsxdy9ezc1NRXAH3/A2hrh4VVd02Nj41isNsbGU42N3ZnMf2bPljOx+a1bt1isgUQg4rPZnoWiW/k3H6j/n10Rvn1LixdTeroGUTERMRhFxcVar17RjRt0+TIlJlJiIi1ZQosX07Fjj8vKOhBRWVmnmzcfKqX3hATKy6PQUCG3MjMzp0+f3737/B49Mi0sRLbw999/T5t2pHXrwhrOwSvg+9qIiGjdugklJQffvLmybNnR+/fvVy8wbBhpadGWLcKrN2tGkybRhQtka0sjRrzZtOn35ORkIurVi27epDt3qHlzSktTpsC5uTR/Pu3Z46uj8xvRAy2tZYWFvs7O9NtvVFCgzI4kcvbsy6KiFkQEtLh588XixbRrV/mAv3GDbtyg27epoIBBVG56ZjC+qX7/PnXpQuPH09KluhkZx6Kji2JijM+fP6giaVeupCFDyNiYli+nkSN7bNjwi79/exX1JZ7vPuYrGDeODhygjh3p7l26dk2Giu3b08SJ1KMHFRRQkyZNnJyciKhLF0pNJSJydqYTJ74WDg8PvXx5U2JiwKtXZ2/fNrt/n5o2pePHZRO1oIB27cKnT+VjKDubrlyRrYV/C3Lo2H8PQvMRJicjKAhGRggKQnT0CQ7HlcPp5uzcNi8vT2gjW7bsNDbuQXTQxKRTYqIUWS8lUVyM+vWRnCzkVmlpqb29l6bmTqIdNjZeog6Wjhw51dR0gIbGzxYW3qLEFqCKr+P79+X0FxWguEglJSUcjrcgemGdOtP+qp63FwBw8uRrLa0ANtvDz693brWTmIJgYOPGvdLSctXQWFunTuiPP0ZV3I2LKw/jqfh3fF4eYmJgbo6gIDx8iLi4nQEBwTNnLiosLBRkhDA1RURE1aS7qlgRXriAwECYm18yMGhOFGds3H3dus1CSxYWFrq5+XM4/djsZnFx5am/PnxARWBSsW6bSiM7GywWXr7Ex48wMZEqtA1UM+YF/qJyB1ZU7ooQwMSJmDIFq1ahe3eZ6w4ZgkGD8Pbt2ypx/o4dg60twsMh4lQFTp2CoyMCA6X6Q7x/j+hoWFggMJDfosUgNrs7m+0XGLiwXj34+ws/2v9vXhH+v1eELVu2EpwazshAdDTs7ODhgdhYVPxYCgsLX0lKTHn8ePLUqfPOnFE0osznz5937twZFnaiRw/hauTRo0dc7gDBFM/hDHgkLCcTn8/ncNwFZWrXnnVYbDooVUiDzncAACAASURBVEwKc+YolGdOKSJ17hxsaDiFwVjF5XpUV3ICevcewWD8RQRt7Q0zZgixaWdmwtg4VkNDkBeJX8UG+Px5efK/p0/lFLKyCnzwQGSx168RFQU2+2sG3ZycnPXrY7dv3yGfLqz+hgUq0M4OsbEoLUVqaurSpTHnxPodCpxlsrKyABQWYtEisFiYMqVGk1gtXlzu6/jLLxg2TNpaqhjzBw9C3uBfgAoUocDh/J9/YGkpc4zDggKw2dMNDduw2e2GD/9mhyM7GxERsLQU6ZVdUICoKJiaCgn1XsHz54iIgIkJQkKQllZ+8enTpwK9W1KC2FhYWsLfHykp31RUK0JVER8fr6e3nMv16NUrl8lEeLjIsCNiyMvL69MnzNraY8CAMWIM3BLJzs62tfXQ0YlmMIb36DFeaJmCggIWy5UonSidy3UtEBFg1NLSnSiLCKamwZcuXRLTqSomhSZN8Pff8ldXikhlZWX79u0LDd0YECAyNoqXVzeiDCIQnQ8OjhBaZt68IwzGUCI+0QM7O78qd/l8xMSAzca4cX/Vq+dTt6737t2ij6oAhw4lLVy4PC0tLT8fMTGCz2Eh+aGEUlSEuDg4O8PBoYjNbq6js1xff7avbzepKn9L5TcsSAJeoQKlhM/nh4X9xOW61avXYuHC63Xrok8fKOlcorSUlMDaGjdvoqAAZmZfp1SJqGLMBwdLiHovHqUrQgChoVi4ECtXondv2Sq+e/fO1LS14Euaze5QPevOuXNwcEBAwC1bW282261Tp4FVDm4+fowOHeDqir//RkpKypw5i//44yiAW7cQElJu4RDvu1BcjNhYWFh8842oVoSqIj4+XlOzREtremTkX3KrsIiImdraa4lQq9ZSRZxlDh06ZGg458tqz4MvwrbYtu15M7MOrq4dzp0TGetk48ZjGhouTKbLiBESjvIpfVJ48EAhuyiUKlJRESwsRGqa3bv3MZltNTVjtLQ8L18WHgLn3LlzmpouRK5E3traFyvncKjg6tUiLS0Xoo9EOaamzVJSPn76JOSc4rRpC4yMhhJtNzDwZLPvBgVBymxwVVi//lqtWqME40RX13/s2E+LF2PzZiQl4dIlPH4MMe8vKysrMHCwtbV7WNhPycmlfn5wcMDWrTJbMv/66686dUYQgehlrVotz5yR50EUZPNmdOoEAGvWoFcvGSoqfcwXFcHUFJnSxnIRgioUYVoazMzw6RMsLXH7tgwVMzMz2ey2ggFmbNxZaIj23FywWAGCvNZ6elFbtmyrXiYhAUzm37VqtSBKMDAY2KjRKjMzREVJCABSmbw8REeDyURQEI4ff9CgQTM9vTorVmyQ4WG+oFaE4oiPjzc0fM1idUqT/nuyGn5+/b4kOr/bufNgudu5fPly7do/EPGJ3llbewgtc/EirK2Fh8+uzKhRiIqCKFVaGaVPCnPnKmQXhbJFWrhQXGie1NTUrVvjvL2f/fab8AKrV69jMDYJJgUTk9nW1lX36gC8evWKw+khKKOpOcTS8o6JCYyN8WUqgYkJ6taFlpY3USkRiPaOHLlI7ifKyMhgs1sRFRF9MjJyXry49KefMGQIAgPh7Y26dQWBGWFpCRcXdOiA4GBMmIBffsGGDWjdepym5g4ivobGDDZ7/bZtckZS3bo1TlPzV4HF2Nxc+FhVKXw+nJxw4gTKylCvHsRaPaqi9DF/4ADat1eoBVUoQgDdumHDBixdin79ZKs4dOiPbHYnY+MuurpjhToroFLScqJtfn4rhH4jDhs2neg4EYjyLCz85NvUzsrC7NnQ1PQjWkvUydS02zVZrb3/w4rw06dPYiIC8/n8d+/efZSUWSQ+Pt7ExGXRIlmSSVdj8eIdGho9iQ5paHT+/fcDcrfz5g0MDGabmLja2XmdOiVku5HHg6cndu+W0E5mpgz79kqfFJycFLKLQtkiffoEU1OI3+RNTQWXKzzrXlpaGovlQ3SKwZjftm3477+jfv2qp1b4fL6Tk5+OznJt7dUODj5VfjaC1eGzZ/Dy6sNgnCPiGxqO37VrjyIPtW7dFgsLNzs7r8OHjwktUFiIV6+QkoI//0R8PFauxIwZGDECdep0IPogsAYPGjRRbgF27MjU0HDV0lpubBwyfvxMuduRmyNH4OICPh87d8qc/E/pY37gQGyQZ5XyFRUpwsuXYW+P7GxwuTLnFHvx4sXz588vXACXi+PHhRRYvnwdk9lZX38+m+3644/ptrbw8EBMzDcJW9at26SvP42Ir6FxsGfP4Yo8C4fjSfQHURcdnRU7duyQtfr/oCIsLi7u168fk8lksVhBQUHF1dL0Xbx4kcVimZubm5qaNmrUSMzzKx5iraAA7u4YP/74lClzBw4807mznFZBPh9dukhIjL5xI1q0kNz+rFkYPVrafpU7KTx4ACsrRUNKKn2ekiZnbESEyPB4V65cGTZs8vDha6yti969w8qVaNCg6ndGXl7eunW/rV69Xkx+7fT0dB+fbtbWnqNHR0qzWBePfF6jy5atrVMnmOiQqWmbEydOydd1UhK4XBw79n7r1q2nT5+WrxEF8fPDrl0A4O4OWVMRKHeAFRbCxEQhuyhUpggB+Ppizx4sWgS557mLF8FmQ6jL3a1bt/bt2yfIdCHwtw8JgYkJgoKQlITSUpSVlQ0YMIrJbNS8eeA7xVL6dus2WEdnIJG7mZn7a9nDuf4PKsL169e7u7sXFhYWFRU1a9ZsbbVg7x8/fhS8dB6PFxkZ6ekp5MCvAMUV4eDBCAkp/+/SUvj6YulSedpZuhS+vuK2agRpKCQmc8jPB5cLYc6koppV5qQwbx5+/FHRRpSuCNPTJQdDz86GpaWErAXTpqF9e5SVYcYMuLiIjFZaM8h9fOLgwaTx438W70Ilhl27YGGBlBRenz4j2Gx3S0u3M2ekDmqpJK5dg50dSktx7BiaNq25hJdC2b9fUbsoVKkIk5Lg4oLcXHC5EGq9lIbr12FmJi6pS2WysspzvFhaYvjwe0ymm67uEiaz/datu+TsHgBQXFw8fvyEunUd5AsW/z+oCH19fTd8sURs2rTJx0dcAI6kpKR69eqJuqugIly+HK6u36SGT0+HmRkuXpStnRs3wOHg+XNxZaZMwXApTAurV6NvXxm6Vu6k0LSpbLs1QlGFU9+AARJyFwOIi4OHh7g9s9JS+Plh7lwAmDwZPj4S0kCqFNVFlhHD9u2wtERqKo4cOWJkFEEEokx7e3E/QFXQrx9+/RUA2rbF9u0yV1fuABswQFG7KFSpCAWbqcnJmD//6ye7HAh0oZhY9tW5cwdubjOJ/iACUVaTJgqcLwGg9hqtgoWFRYVB5ty5c2ZmZtXLFBcXx8bGRkVFubm57RedfCEuLq579+5PKyF97uMLF2BmJsR14sgR2NpC0u7kV/Ly4OgI8UbvJ0/AYklO7S1wHJBpi06Jk4JS7KJQjSK8dQtWVhLiovH58POTMKm9eQMLC5w4AT4fYWFo3162sFJZWVmpqanikwRJSc0rwthY2NqWGxvi4rZpai4XJOWwsKhRZ5mnT8FmIy8PV6/C2lqeWHdKHGBKsYtClYoQQFwc/P2RnQ02W2ReUmm4cQNmZpJTblUmOjpGR2cFEYj+5nJ/EOZ/KgP/ZkWoVfOxbHJycvT09AT/bWBgkJ2dXb0MgGfPnn369Ck/Pz9fdMqAZ8+enTx5sn378phMWlpasbGxXl5eEmXIyGD07au/YUMRm83Ly/vmlp8fde1aKziYkZBQVCX6lFDGjavl4UHduxdXaacyERG6Eybwa9cuEVOGiPbv1zIz03ZyKhRfrDL5+fkMaaSUgu3bdXr0YOTnC49VLz1KFKmCevWoXj29bdtKf/ihTEyxJUs0AgP1OnYsYLGEp66oXZs2bdIcNEj37NmCpUsxYoRu3760fXuRlhS/g2PHkkePnk/UxMjowdmz+42NjeV7FgFFRUWampryReKX4w1v3KgdE6N95EiRuTk/O5suXw5gMAL19D4XF99p165XnvQDTmGio2sNHQqgZMEC3YgIXnFxabGMI06JA+zwYS1XV219fRl+cUIpKCjg8XgaQrN4KEy3bjRrlsHt24VhYVq//MJYv17OX2iDBrR3r0afPnqFhcWBgeJ+RxV4ejoBI4g2aWgUN2682MMDISGlkZGlBgYiUsOIpaioqKysTL7BxufzCwoK5KjL5/Nr1aoluZwcOlZB7O3tjx0rd5Y7ceKEnZ2dmMKXLl3S09MTlchRPtNoURG8vLBsmcgCJSVo3hyrpfBF3bcP9eqJDFkk4ORJ2NtDmoRf3t7C97TFoJSv48LCwoSEBFvbQ+fPy+WMrwKRqnP0KJydJS9YJ0xAWJiEMvPno3lzlJSgpARduyI4WKpAa/XqtRZ4bGpq/rZkibiEedJQkyvCRYvg4FAeNys3F926wd8fGRm5SUlJc+emuLjUUEA1AB8/lgdMefIEHI6cpmmlDLA3b9707RtuahoYHi4uhIKUqHRFCGDlSvTrh8+fwWLJ4EAgFMG68IB03vH9+//8JYh8TqNGbTIyEBICKyvExcljOvo3rwi/Q9BtZ2fna19CyV67ds3Z2VlMYVtbW4FbjRIFGDeOrKxo0iSRBbS1accO+uUXSkkR105GBo0ZQ9u3k5GRyDI8Hk2cSCtWkMSPktOn6fNn6tJFQjGlU1ZW5uHRYfDgtPT08zNm/DuiDgujc2diMOjkSQnF5s2jo0fp8mVxZX7+mbhcmjGDtLUpMZHevKGRI4u3b9+5deu2gkoRsktK6PJliomhH34ga2t6+ZJBxCciPr/s40clL3lVx5w5FB9PZ8+StTW9fk2tWxOXS0ePkqWlYbdu3WbPdmOxaM2aGhJm9Wrq25fMzGjxYhozhgwNa6jf6vToMWLfvh6fPq1LTFybKghK/S8mLIzOnqV372j0aFq6VKGm3N3p6FEaPZr27RNe4NMnSkig4cPJyoqSk620tARz9VXA2sKCtm2j7dtpxQpq147u3FFIkn8XcuhYBfnrr7/YbPbp06fPnDnD5XL//PNPwfWWLVtevnwZwJ49exITE+/cuXPq1KkOHToEBIhMASPHinDDBjg6igveUUFiIurXF7na4/HQrh2ioyU0snq1tDEMO3fG779LVbIyin8d37x5k8UaJjg8zuV2eC/0OF7NiiSKrVshzQfltm0SvGYAfPqEunWxbx8A5OfD0NBfS+sXXd3FDg4tDxwojYqCvz8MDdG4MUJCEBuLu3dx7NgJLtedyw0yM/NlMj/HxCi0n6rSFWFpaemDBw/y8/NnzULjxuWb01euwNJSyIh99AhMpgRXL6VQUAAOB2lpePsWTKbwc5/SoJQBZm7uKRjz2tqrtm6NU7A1Va8IAcyejZEjkZUFJjM3Pj5ZaJhi6bl1CyzWHS7Xh81269ZtcHFx6fXriI6Gvz/q1IG/P6Kjcf06SkpKevceYW7u7uratVmzDH//8nNHPB7i4sDlIiQEglMVT58+fSPJCeLfvCL8Pgfq4+LiWrZs2bJly61bt1ZcHDRo0O3btwEkJyf36dPHw8Ojbdu2UVFRYo52yaoIL18Gl4vHj6UtP2qUyJgOv/wCPz/Jsy2Xizt3JHeUmgozM3nygSk+KWRkZLBYrYlKiPLYbJciaWy4KhZJFBXRKcXD56NNG8mhI69cAYeDp08FoV66C6ZFBmNw69ap8+fj1Ckhhru8vLynT5/yeLy0NHh4oFs3+f0sVKcIX79+bWfnyeEM1Nd3a9ToisDta+9esNkiHSXmzEHPnvLJIhUZGRn+/v3ZbJ8mTX4FEBmJCOGhYaVCKQOsfft+DMYGootsdvPH0s8IIqgBRfjhA5hMpKa+MzJy19GZymK1//VXEeGUpMPRsQPRIyJoas40NIx3dsaUKThxQuQmTmkppkyBre1Xp/pPnxARATab36hRsCD7RGTkAjE9qhWhqpBJEb59CxsbHDkiQ/uFhXBzQ/XwXYK8rxJ9qMaPx9ixUnU0eLDkxaVQlDIpDBu2XlvbxdLSfdcuJeyXqE4RolK+AvHcvQsOBxJPAP/6K1xc8PFjAZvtSpRDVMBmN5dyTVxSgunTYW4u7fGsKqhOEYaHz2Yw9hKB6JG3d08AMTGwsoKYaaS4GI0ayeZPKBMtWnRnME4RFRoa/nD48EkWCy9eyN+aUgbYjRs5hobzg4LGXLyo8IGhGlGEAMaNQ4cOGwRZxImKbWy8FGmtIsQagxEfGblcylpHjsDMDEuWfDWHJCTc1NYOFoTrMzb2/Pz5/2Vi3v+KIqx8hkwmHj0Cm/1N3OfcXDg4ICFBQsW0NLDZUtl/KpJ0y4FSJoUuXRCnqHHoKypVhBUZ7CQycSJGjJBcLCgIo0YhMfGQjU0za2vPzZt3yiTP+fOoWxcjR0Js1kghyKcI8/LyhgyZ2LBhq+nTF1TMvBkZSErCnDno3h2WlqhVaxaDsU+gCJs37xEaCk9PyUd3Tp+GjY2qDlZaWXl+WXBvDQxcNWiQQq0pZYBNnIiZygstVzOKMD0dtWvv0NOLIgLRPw0atFSktSVL1piaBurqLjYzcxUamFsUr17B1xcVZtLKGeq1tDz09ApdXTFsGNaswaVLX+Mqv3jxwsOjraEhMy5OnoP5akUoDmkU4dGjR5cujRk8+Em3bnJmYd21Cw0afN1WDA6WKgRap07lp4YlMmWK/HGuFZ8UXr4EiyU5Drj0qFQRApg8GT9JyMkhEANWVpLjA+TmwtFRoe+A7GyEhqJBA1y5IkMt+RThiBE/aWuvJcrV1Y3o0mVLly4wMwObjY4dMX06EhPx7Blev35tatpQW7u7gUEDN7fzvXtL+8cdMkQJcYWEMnBgBIMxnegwi+XF4dyXI1daZRQfYAUFUHBVWoWaUYQABgwoqVevD4vVQlPT5bffRKavkZLr16/v3r1bjthppaWIioKNDS5cAJ/P79ZtMIcTyGa3mjlzcWEhrlzB+vUIC4OHB/T00KQJQkJgYeFPtJgogMkMuC1TNg0AakUoHomKcMqUeUZGQxiMrZqa7teuyX8YdehQ9Or1YOnSmKlTjzduLHlmSUpCo0ZSHRaWfokjFMUnhWnTMHmy5GLSo2pFKMhZKk3+2Ph4uLtLzs9w5w5MTZ8FBo7v12/0fXnDWCUmgsvFvHlITj4dHj5106Y48bEd5FCEPB7s7AKIPhKB6ELTphMOHBAycrZv32NkFEz0F4Mx08VlpvTz84cPMDPDjRsyCSUVc+eWeHj8PmHCrNmzb3bpomhrig+wLVvQTZ5EkCKpMUV45w7MzfHxY/5vv0kVuFilJCfDwgJRUeDxcOrUqZQqeXgBACUlSEnBpk3Q168Iur1yu+zxhNSKUBwSFaGNTTMiHhEYjJ3z54s+OSiJK1fuaGp6EsUxGANGj5bQTnExGjTAF2dYCSxZAkUsRQpOCsXFMDNTKFxFdVStCAEMGiRtSNi2bbFunYQyZWVlpqYeRMlEZyws3EVlS5bI27do3vycllY7opOGhhFTpswTU1gmRZidjdhYNGwIC4ultWqNITpnbNw1KekPoYVDQ38kOkcEos9OTrKF0dyyBZ6ecqZ2EsXbt2Cx8PQpeDw0aoSzQvKyyIbiA8zLC38If3lyUmOKEICj4zout2XXrkMbNSqV8jig6nj1Cr6+fBbrBxarN5vdftIkkZtP/v4/aGmFE3lzOM1eyL4YVytCcYhXhJcuwcAgkOgaEb927XGJiXvl7mjatF+IDhCBqERMbMYPHz789tumoUOTunaV6ldRUgIbGwj7kJIWBSeFHTsg+nCKnNSAIrxzB1ZWqJa2RAh374LJvD9p0sL4+J2ilmjp6elcbq8vzvTD+/e/tX49Tp+u6hFaUFDw888LO3UK3bdPpFfJ+PEzif4U+DLY27cQI5iUijAtDaNGwcQEwcG4cgU8Hm/jxrj+/cccOSI8ZUNKCurX36upOYDovp7erEmToiR2URmBw62UJn0pGTkS48eXTJs2v1mz4Q0bCksIJCMKDrBbt2Bjo2RlX2OK8OTJk4aGQUR5DMafTZvOrldPqkgdirB69cYWLXpNmhQl6gPx2rUUPb0QwR4hm+0lqlheXl5IyHBLS7tbohJti0WtCMUhShFmZGDQIFhZYeXKpx4enSwtPUaOnKpI6pwtW7YZGEwhAoNxvk0b4VGxs7KyrK3dNTVXMRgRnTqFS9OslAfjxKDgpNCyJURHcpWTGlCEAAICpNrYe/Lkib6+B1GCoeGkIUOEJ/ArKyuztHQjukyUwmS6r1qVFxEBf3+Ym8PYGB4eCAlBdDTatBmnrb2U6I6JSedz54RnbIiP31W79kiiAgYjvlat8DlzRJpwxStCHg+HDyMgAIKc4FVcXYS+4fx8REaCzUZMDDZs2BoQEDx37tLqOc4k8vAh2GwJCSClR+Ay1rnzOB2d1UQptWu3EWpAkwkFB1hYGBbJn1ZZODWmCJct+5XBiBN8tFlZeXbqhJgYFXaXkLDPyCiY6GWtWkvDwqYILXP79m0Wq78gbq2WlvvevUWiJlq116iqqK4Ii4sREwMmExERUp2alxIejzd48ARLSw9v78B0Qayqahw4cFBXd96Xk+me0uhdFxecOKGQYIpMCvfuwcJCnqjH4qkZRXj8uFQZfNasWa+hsVHwRzEzE5nPKy0tLTBwSMeOg25+68jx7h1On8b69Rg/Hrq6zYj4RCDa36PHfKFh2fl8/syZ0Q4Ovv36jbpzJysiAiwWIiOFuASLUoQ5OYiNRaNGcHdHbCyEfl5Xf8P798PGBkOHyn9KvTJRUejdWwntAOjSRXB4o8JrNG758lUKtqnIAMvJAZMpbdZr6akxRZiamspi+RAlMxhR/fuPExwTUl1CseDgSKITRCAq4HBaPX0qvFjv3sM5nA5sts/Agcs9PdG0KeLihKy51YpQVVRRhElJsLdHYCBE/cFUxJs3WLgQVlbXNDV7EZURvba1bSax1tGj5Um6FUGRSWHMGHnOk0ikZhQhADc3HBOe4P0rJ06cMDYeSFRKdEVLq1NwsLjjdOLp2zdcW3sD0XN9/Z5+ficEi8WoKAkNvniB8HCYmFRVh9UV4ePHiIwEk4nAQCQni2uz8hvOyEDfvnBwkFBFJoqK4OgoW8oeoZw8iXr1UFwMf//+DMYOopcmJp3lzqRYgSIDbM0a9O+vYP9CqMk9witXroSHT3V23jB/fjGAkSPl9zkXw507CA+HkdGRWrV6E6XWqhXl4jLDwgKNGyMyEufPV524Xr9+/fHLt2FyMry80KRJVXWoVoSqIj4+3tnZOTMz88EDdO4MR0chLiqFhYWvXr1SPKV4dQQ5nYOCYGqKkBAkJ2PGjGhzczcHB9+LFyXnUmrbFjtlO7QmBLknhdxcmJoiI0NRAapTY4owPh7+/pKLzZmz1Na2ma9vj9TUZzExsLWFry8SEmTeJcrJyRk7drqvb+/Nm7cDKCxEcjIiI9GwIezsEB6OpCRkZRW0atWTzfaoX9/nwYMHFXWfP0d4OExNy9VhVlbWhAkTZs6cWVpayueXjyIzM0RGSvYfzs3NPXnyZHFxcWkpYmLAZiMqSvkbRWfOwNZWoWOFPB7c3csN79HRHy0sJvn49Ny+fY/isikywJo2xZkziotQlZpUhAKePgWLhbdvkZmphEjclTl/HoGB4HIRFYVPnxAfv7tTp9B585YXFxfzeLh+HVFRaNgQtraIiEByMkpLsW9fUqNGrT08Ol2//tXnODkZzZujceOv6jA+Pl589lkxqBWhOOLj4xmM+RoajdnsvJgYIUH0//rrBJfryuV2c3ZumyfrmedvSUjY36lT6KxZ0YWFha9eIToatrbw8EBsrDzzxbVrciZjq4Lck8KGDejTR9HehVJjivDVq7e1anVlsTw6dBggvasnj4ekJLRoAXt7xMR8PQjP4/GkT2ZZhTt3sGgRfH2hq7taQ0OQv+2Ws3OvW7fw9Ck+fChXVAJ1yOGUaGo2JfqZwRhtYNCicWO4uYm0glZh586dGhqWGhpttbTqNmiQ0amTEONHbm6uj08gm+3h6NjquQIhRENDFVpq/P47WrUCZAk0KCVyD7Bz59CokUqOHNS8IgQwcSLGjAGABQvk/y1XiM3jYe9eeHmhYUNs3Cj50+r2bcyZA1dXsNlvdHV9iD4SPTE3d6uy5Dh/Hm3bwt4eLVsuZDDMGAyTOnUcCmUPJqlqRcgA5Mks9S9h+/btISGDiH5q3NjCw2OSIMuhpubXdBCbN7d+/34/EUtbO+bXXw1Gjw6Tr6OTJ0/17bvy8+dF2tp/sFhvy8pWDhpEI0ZQ48YyN3Xz5s1JkxalpTFCQ6ctXeomnzwV5Obm1q5dW46KHh60ZAl9yeSoTOQWSVb69Ru1d28gEKijs3ratMK5c6fKVP3iRVq5ks6epREjqFat39atW0vEGDs2NCpKdF4SSYwdO3fdOleiHkQ5enrdHRzO5OXR58+Ul0cAGRqSsTEVFSX8888xos1ERNTax2eXlZUlEenpka7u16a0tKjKWzQxoTlz/IqK1hE1IUpkMvcvXLircgFtbTI0pMTEJQcOGJWVjSK62KnThj//jJfvWd6/pyZN3vXtu61+fd3w8CGGsqSKyM+nhg3pwAFq1ozGjyciWr1aPimEIPcACw6m5s3L5VEu+fn5enp6KspHKIqsLHJ0pFOnyN6eHB1pxw5q2VKG6jdupPTuPbKwkJo2te/TZ3tMjDaLRVOnUvfuJNNzJCZeGDJkb0FBDBExGP5E+4yN6xgZUe3aVPHv/Hw6etSZaB7RRqL6s2cbz507V6aHdXNz27Rpk4eHh0y1iIjP5/N4PImJP79DYl7lwmCUEV3v3DnCxYUKC4mIysooN7f8bmkpj6gWEZWW6kZHl2RnU69e1LChbF2kp9PSpRc+fw4nciotbVxW5vPqleS0SkIpKirq0mXY27fbiGj79sHz5/8tVdJIZfP335STQ+3aG0hAHwAAIABJREFU1XzPyuTFi9eAOxGVlLg/ebJH1uq+vuTrS8+e0fLleevXbwRuEGmsXdsuLKy/hYWFfCJNmDBg374fcnPv6emdWLYsfMiQr7dKSykvj7Ky6NIlm9DQ20AZUTGD8WLsWBMdHSKiwkKqnG1MUL4yWVnE44FIoC11iTJv3PimgKDKjRsfy8oEH1j1L1z4sGoV9exJNjYyP4uhYSGf33XDhnHa2jlxcd1v3z4lfd1ly8jPj5o1o3v3KCGB7t+XuXel8+ED/fknrV37veVQHiYmNG0aTZtGhw/TwoU0eTJdvkzSZyweOnRqenoikd3p0798/Lhz8+bBMunRCjp3djU1nVBYuFtLK6tJE8aNG3Wysyk7m3JzKSeHcnMpN5eysujPPz9/yfqnVyxrLuYaQI7F5r+H+Ph4DY1GPj4io0Ts3r2PzfZmsUbUq+d99OiniAhYWcHeHhER3+z3vn379sSJE5WjLRcUlO8AeXiAzUarVsf09PoQvdDWXjNgwBi5BX706BGHM0DgQcfhDHio8FF2+cxEISFYsULBnkVSY6bRuLhdpqYdGIzfdHSaX7ggvwvG27dv2eyOgj+Kpmb/n366LzEyp/jW9u3bl5aWJqZMx44DNDSsNDWtIiJ+lr7lv/6CkdF6BsNOQ+MHTU2Lv/8Wvg+dmprK4bjp6y9kMltNnbp/6FCwWPD0xPz5uHfvazE+n//8+fNs0Umlr169ymKN/uIF3Un6/FyvX38NYBYQgFWKeolWRb4Btngxhg9XsiQVfBfTKIDiYjg44Phx8Plo1gw7dshQ19q6OVGRwJU3OnqlImK8evVq2rRfFi5cIWY49ew5hMGwZTC4enp2WbIHVlbvEYpDmlijmZmZKSkplQ9U3b2LqCg0bgxra4SHY/HiS2y2h7HxVA7HLSnpRmwsAgNhYAAPj3L/KMEI37gxzte3d0TETEX2GktKSkxMPIj+YDD+sLHxkDv/QAVyTAofPsDEBEK9/5VCjSlCANeuXVuzJtbG5r6Cp1A6dOhvahpuYjLW2TkwIoLPZsPfH3FxygzBWgVZQ6zFxsLcHKdOIT09ffPmzR/F/v1ev369d+/eipR1ZWU4fx6RkXBwQN26iIjAqVNFHh4dOJxebLZnfLzw+PGZmZlsdnOiz0SvdXRc3r6VdgN12DBERgLAwYPSBhqUCTkGGJ8PBwdcu6ZkSSr4XooQwN69cHEBj4dLl2BtLe2IPXMGdeps1Nf3NzSMsrR0y1CF11w1Vq1a5erqWlrdlUMK1IpQHHIk5q1MWhoWLECdOiFEt4lAdMXQMHzsWBw6pKow/Ldvw9T0ed++k4cPnyxHnKHqyDEpLFmCIUMU71kkNakIBezeDTc3OSOqC+Dz+WfPnj116pRgOsvPR3w82rcHi4Vx474ekLh06dKWLVteKeO0ufSKMC8P/frBy+vrIXdF3vD165gxA9bWiQzGbCIQ5Vtbizzqk5BwsH79Fk2atB069KKNDUQsQb/h9m2YmeHzZxQXo359/PWX3JKKRI7HP3YMbm7Kl6SC76gIAbRqhS1bAKB3b8mxAvh8xMTA3BwnTyItLe3QoUNiEr4qF/XxCVWhoCIU0KPHUKJkIhAlBQdLlz9QLnJz0bChbOYLicg6KQg+jWXKkyArNa8I+Xy0aKGEsyjVefECc+eiXj04OaFr11XGxj01NVdxOK6Kp3KVUhE+fQpnZwwb9o0Xn+JveNeu3V+CPxQaGHj884/kKocPg8uVHMekQ4fy4K5Llig5sHUFcjx+jx7YtEkVspTzfRXhlSuwtEReXvmZCjF/zdxcBAXBw0P+KP+K8G9WhDXq5vTvpFYtTaIfidowGD/XqqVC76Fhw8jfnwYOVF0Pkjl+nAwMyMvre8qgdBgMio6mn38mpe/B29rS7Nn0+DGtXUvnzu38/HkPjzf+3bvp27fvU3JPwjh+nFq0oJEj6fff5XTOEkWPHt0bNDjNZgczmW1atvzRyYnmzaP8fHFVAgPp/Hn6/XcKDaWCAuFljh6l9HQaMYIyM2nJElq+XJkyy01GBl24QP37f285VIaXF7VuTcuXk709DRpE8+YJL/b4MTVvTiYmdOmSPM5T/9uoFSE9e5ZPtJfoCBB/+nTuzZsq6WXdOnr69PtPDevX07hx31kGVdCqFbm40Jo1KmmcwaDWralxYxbRfSJiMG7fumUhcFFWEQAtXkxDh1JiIo0Zo/z29fT0bt48cebMjIcP/zh2LPj6dXr4kBo2pI0biccTWcvBga5cIU1NatmSnj+vepfHo8hIWrqUtLVpxgwaMoQcHJQvuRxs3EjBwWRg8L3lUCVLltCaNfTPPzR7Nu3bR3fvVi1w5Ai1akUTJ1JsLAm8lNV8gxyLzX8PCppGX79GeDiYzKMGBq00NVebmPiMGHG6fn00boyoKGXm7bx9G2w2KkUaURoymYkEmfwUiysgmZo3jQp48ABsNj58UFX7Dx8+bNy4jZmZe9euIwYNKrO2ViidrxjTaF4egoLg7i5yBKroDV+/jrZt4eiIBOHeM1+JjYWZWdX4duvXo00bAEhJgbm5VAkj5UOmxy8thaUl7t5VlTACvq9pVMDUqeVusStWoHLSRz4f0dGwsVHthog0/JtNo/9RRVg5Nnd2Nu7evbtly5aKmFjXryMiAhWugwLN8fDhw2XLfk2WPaSjKrYGK5BpUpg1CxERKhGjMt9LEQIYNUrJSYbFcPo0mjSBvz/kS+UrShE+eYKmTTFokLhAMyp9w8nJcHaGjw8uXsSnT59WrVq3cePm6oF7zp+HhQUiI8t9lHJzYWFR7lXUurVqN+Rkevx9++DnpzJRvvBvUIQ5OTA3R0oKSkrg4MBfuvTMqVOn3r/ndewIPz/lxxmXA7UiVBXyKcKkJNStKzk2d2EhEhIQGAgmE9263alTx5Noa506A+bOXS5TdwMHYtw4WWWUFuknhZr5NMZ3VYSCuItPntRQdyUliIkBh4OoKMgaNEqoIjx2DBwOoqMl1FX1G+bxEBcHC4siAwMvTc01urqLPD2FzF+ZmWjbFu7uh8zM3PT1nV1cFgPYtUtRD16JyPT4/v7YtUt1spTzb1CEANasQbt2ANC0aV9NzVFGRmN0dXtERPDlOrCgfNSKUFXIqghTUtC6NVxdZQu8+/o12raVKjFvddasgbu7CpNnSj8pJCaWW65UzXdUhADmzcOAATLXOnfugouLv5NT22PHZF7xp6ejb1/Uq4fDh2WoVUURCuxXFhY4f15y3Zp5w+fOXdPXHyU4UM9idfwgzOhcVMTT13cmyiXimZp2vnXrga0tRORqVBrSP/6TJ+BwVJ66Fv8aRVhaiiZNsH17BofT9f/YO/O4mL43jn9m2k17TakIFSIpspcltCHZUvYtRMku8bVkbbGFnzXbN3wREdkjO6EoS7YSon3fl5nz+2PsKjXNzJ2471d/zNw5y+dOd+5zzznPeR7eP05JadgbkT0Y/g5xNoR/uLPMixcvWrfupaVlNnjwNBcXbr9+GD0aDx+iZ89aNKKtjREjtJnMCABAZGpqwzt3alQxOhorVuDYMQG7/PHH9u2YNo1qEcJnzhzcuIEHD2pRpayszNHRLSZm39On/40duzA3N7dWPTZujOBgbN+OefMwYEBx586DNTQ6NG/e7eXLlzVsobAQzs44eRL379cuXKRQMTDQYbFigSIgIysrefFipXfvfi7D5ZbKy8sD8gATaL55c1rnzujenQq5lbFjByZNEotfn2iQlMTatVi5Up7LTQc4AFdKKlk0gX/rO3+4IRw1alZc3LaUlKjQUIWUlOAXLzB5MiQkat1OUVEJg/Ec6AB4q6kZjhiBESPw/n11VXJz4eSE//0P+vp8yxcYL18iLg6DBlGtQ/iwWPD2xvz5taiSmprK4TQDGgGaXK7Ju1/v9zXAygqxsSBkz4MHPdLTH755s33SJK+aVIyPR9eukJfH9evQ0eGjZ2GhpaXl5zejSZNezZsPOnJko5qapJkZXFwQH/+tjJycXOfOzZWV3Vis5SoqUaGhnfz8qFP8IyUlOHAAk/kMs19fsbdH48ZKvXqNZ7Pbs9ntZ80arqGhQbWoekC9D7r97Nm7+Ph4/e+sTUICoqIQHY3oaDx+nA20AACYmJt/VFLis5fMzDwu1wVwAgokJe2ePMG6dTAzg6srPD1RaVz+yZNhY4Nhw/jsUbDs3ImJE/8Wt+nx47FpE86cgb19jconJzfKyUljMvcSIpWX90Rd3ZC/fqWl0apV5rlzpgCAZnfvZllYwMzs85+h4bcnsA8fPmzYsEtBoYGJiaubm8rSpULZI1F3xo93Hj/+8/47R0fMm4eAAHTtCltbLFoEQ0MACA3dFx4enp+ff+yYZ4sWMk2aUCn4e4KD0bEjmjWjWofIWbcOtrbTXr2aqqBAJPh46v8rqfeG8NUrd3Pz4Rs2XH71SjUqCpGRkJT8fOtxd0erVsP//XdEXl4PdfW9zs4hfPdSVjZCQmKQjMxzQm7k509LTMTSpXBxgbc3WrXCkiWYNOmHgebWrXj5EgcOCOAE60h8fPy5c1f27m39+LHYTLoJGQkJ+PpizhzY2UHydxd4cDDc3Rn79p1JSNhTUcEpLj7Tt6/0lSvQ0uKn66lTRx48OLyg4Kms7JXVq6caGSEqCuHh8PVFQgL09GBmBmPjonXrBqWnezEYuUzm4EuXrlla8tOX6FFRwfLlmDcPe/agTx+0bw9vb7RsWfTkyaunT/Nv3uyyZ48c1RoBIC4ubvXq7VeusHx9PQC+/pH1GRMTdOjw0Nh4daNG3A0bvLp27UK1ovoAH8uP4kNQUBCTWc5k/mNhcX71anL+PElL+7nMzZs39+3b9/HjR/664HLJvHmkdWvy/HnWmTNnXr16deQIYbNJWNjnAvfvk27dSIcO5OZNkpCQsHjxai+v3Wx2iWiWqKt3HHjy5AmbbcZg7JSSGuznt1UUgqh2lvlK375kx47qCnzdXxUV9cNxHx9iaEj4vV5ISkpKSEhI3C+bKnJyyNWrxN+fWFndk5CYXr0TSvWIwzdcUEDWrSNaWkRFxVZKahNwSFW1vWiEVd9Lbm6ullZ74CpwonnzbiLQQ8TGWYZHSUkJm20KvABea2q2KxRe5PhaIs7OMvXeELJYL9XVuyckJAijfQ6HuLqSTp1+ztVw8yZp2PBzTEVCCJdL/vuPNGqUKivbjsE4ymCsMjFxFoaeX6n+pjB/vjcQygsp2by5uThIEhlRUURbu8rg6YWFZNgwYmFRyZMTIWTlStKqFalLMqZqSEtLY7M7AanAa21tUz5uoGLyDRNCMjIK5eS68Yy6rKx7RESECDqt/vTv3LmjqjrjS/Yofp4z+ECsDGFCQoKmpiPvG2Czx1SfEUyUiLMhrPfOMvLyI/73v9nNhLAUUFGB8eMRF4fwcKiq/vCRhQVu3cKWLZg1CxwOGAw4O2Pduttc7jBChhOyOCUlnhAicEm1RVu7EZP5EAAQ3agRn8lm6ynt26N3b6xbV8lHSUno3h0KCrhyBWx2JQX++QdjxsDSEp8+CV4Ym83et29F69ZOZmYeoaGBIk5rLlhUVGRLS98Bb4G8srLrVMsBAC2tFvn5d4GXwH0ZmUzVn366fwG6uroNGiQyGCcYjFO5uXHKymLgrSf21Ps1wj59DIcPHyzwZsvKMGoUCgtx/jzkKlv40NdHZCScndG/P44ehZISWrc2UFDYmZk5G3inoCDFqHmuaOGQk4P//hvbtKlbcbGZlhZ7376d1OoRPatXw8wMU6f+sOAXGYmhQzFrFubNq66ulxckJNCrF65eRaNGAhbWv79Nnz49JSQkpKSkBNy0aCktLVVQYOfmTgWKGIxmFy9KdOqEBg0o05OejmHD1BwcNqWmLpaXb7Bx40HKf4aiR0JC4tatU76+27lcroTEyb59pcPD+Vzz/nuo94ZQGBQVYehQSEvj5MnqNiEpKCA0FLNnw8ICZ87A2NjY23v4hg29VVVV9u/fJUK9lZCdDRsb9OghuW7dX2f/vqKri4kTsWwZdn35bxw7hhkzsGsXHBx+X33BAkhJoXt3XL36Nzof1oSYGLmSkrZycgpMJltV9fLr150aN8awYXB1Rbt2ohaTnAwbG/TrBx8fc8Bc1N2LE9ra2gEBK3mvfX3RuzfCw8Vrc464QRvCnykshIMDNDVx4MDvfQ4lJbFlC3btQteuCA6Gm9tEN7eJIpFZHWlpsLKCnR18fKiWQjVeXtDXv5eaerRbt2YVFVN27ZK9cKEW9+jZsyEnh969ceUK9PSEKbQeEhICV1ccP75fWvpyfn5+v34L5ORkkpPx778YMgRqapgyBSNHft5cVFZWlpOTI7w9bW/fwtoarq6YO1dIPdRXPD3BZKJ7d1y5Qj/PVQ0fy4/ig0AS835Pdjbp0oW4utY6WCIvSuScOWGdOw8cNGjS+/fvqyoZGnpOX7+rvn7X06fP11VuZY4DKSmkTRvi6Vn3tvlEfFw5CCHPnj1jsboCERISa9TVJ6en89PIli2kWTMiWH+smmeo/xVx+Ib9/UnjxuTRo8o/raggYWFk4ECiqkqmTyf/+98lDQ0TDQ2bTp3sSuoc8ezX04+JITo6ZO/eOjbMP2LlLFMp27aRpk1JnfNJ1wlxdpahR4QAcOfOnZs3I01MzD09O9nYwNcXtV1ZsLHBnj2vHRx8udwgBuPNq1fjb926AiA7+4diRUUF48cvzs6+AsDFpW9CQneWQPOkJSWhTx9MmICFCwXYaj3m6tXrRUUuQC8Op5e0dEd1dX4acXfnrRcWWln5Jya+njDBYdSo4YJWWm/gcODhgVu3cOdOlaunEhLo3x/9++PjR+zZg5kzl1dUXAVU8/LWHD0aPHbsaAHquXsXQ4Zg61YMHSrAVv80pk2DpCR69sTFi2jThmo14gdtCHHkyInp03dnZ4+RkPBycprr59ePv3bKyp7IyNgVFzchpElcnJe+PgEYKio/lCkvT8nPbwGoAMjJab56dfLEiQYGBnU/CQB49w59+mDaNHp26Bvt2rVVVvbLzh7MYNxr1oz/RZJp07B377y9e1sTMiIqar62toalZS/Byaw38MKilpXh5k0oKv6+vI4Oli7F3r2cd+/kAJSWyvv5lVRUYNCgnz2x+ePyZYwejaAgWFsLoLU/m8mTIS8PGxucOwcTE6rViBn12HVbUOzcGZydvRUYxeEE5OYG891Op06dFBROArckJfd37qyZlcXIykJ8/A9/iYl6BgZJUlJbpKS2aGgkZWbq9eoFIyMsXIg7d8DlfmstJyfnw4cPNe89IQG9emHmTNoK/oC5ufmaNYOMjIY5OJw4fnxHXZpKT48ixB1omZMz4dKl24JSWI9ITkbPntDURFhYjazgV5YuncFmW6mpuerp/Td/vuOFC9DTg50d9u5FVtYPJUtKSmre7MmTGDMGJ0/SVrCmjBiBTZtgbY3ISKqliBnUjAg/ffq0ZcuWzMxMGxubob/MaJSUlJw9e/b27dslJSWdO3ceNWqU5G+9VvglIQGJic0YjFuEGEhL32jViv/V5EaNGp07t93ff2+jRhr//BNUaRkmkxkZeW7//kMAJkw4r6DAJARRUTh9Gm5u+PQJAwbA3h5v3+5du3Yng6HRurV0ePix3wYMfP0affti0SJMncq3/D+WsWOdWrZs2qhRo4YNG9alna5dOyQnby4rs5GU3BsSMtfBAV3+pthVz55hwACMG4fly2tdd+LEUba2lh8/fjQxMZGWlh43DsXFCA9HcDDmzoWRERwdYWGR6OQ0Ij9fgs2WvH79hJqaWlWtpaSkyMnJHTkiuXAhLlyAqWldTuuPhRdQRv6XOMiOjmCx4OCAkBB06yZSSeXl5aWlpSLtsubwsfxYRwoLC5s0aTJ9+vT9+/c3adJk586dPxU4e/Zs9+7d/f39d+7caWRkNGrUqKqaqouzTGEhWbyYqKsTb+9ca+uROjpm9vbjqA1HlJhItmwhVlaEwTAFSgHCYs3Yty+8tLTy8teuXduwYdOlS/GNGpE9e0SrtWrEwZXjK5mZmU2bdlBRmauu3mfDhmpDrv2OgoKCuXOX9+078vDh4P37iZYWmTKF8B23pF44yxQVFR05cuTUqVOXLpVraJCDBwXfRUEBOXqUDBtGpKQmAdcBwmQedXRc/OoVyc7+uXBWVlarVhZsdj9FRVMtrQcvXwpeD3+Im7OMl9daNrsjm91pzhzvSgtERBB19dd6en21tNpPmDBbBOIPHTqupKQnLa1uaTm0oqKiVnXfvXvXuHHjbV9DedUG8Q2xtnfv3o4dO/Jenz59Wl9fn8vlfl+g/LuEyo8fP5aQkCgqKqq0Kb4N4dGjRFeXjBrFf0jJSjlz5lyXLg5Dhkz+8OED341wuVwNDVOgHCCSkrO1tS/KyhI9PWJtTaZPJxs2kDNnSFwcWbFik7KyI4Oxi8ls7+srLlGUiJgZwt2790hJBQAEKG3cuKMAW87JITNnEk1Nsn07qeXvmpD6YAjLyspatbKQk1shKztfWnpwVbmsS0pKdu/eu3795nT+XHK/YGHhCLwCCHBDXX2Gvj5RVCSSkkRTk7RuTXr0IIMHk/bt/RiMPQAB4k1M+telO8EiVoYwMzNTXb0bwAUIm22ZnJxcabFWrfoD0QCRl5915MgxYavS0WkPhAD95OTmzp8fduwYuXiRXL5MHj4k0dEkPp68e0eyskhBwc8V4+PjNTRMAV0FBTsvrzW17Vd8vUZv375t+SXevqWlZXx8fGpq6vfTVt9PhGZlZbFYLBnB5dZ8+hQeHsjOxqFDAk6C+ubNmwkT1mZkHGAw3rx+PS429gp/7TAYjHnzJvv792IwdPT0Cm/e9CMEiYl48wZv3uD1a1y+zHt9lJBrgDQh6jk5IcBiQZ7Mn4KCgryUVEJ5OYBcBkOQYVyUlLBpE1xc4O6O3buxdSu6dhVg89QTGxubnt6quHgJADU1a2PjLKAS/5bevR2jo7uUl6sFBNg+f36dby/o5cunjRgxurTUWlb27OXL+9u2BYDycmRkIDPz89+uXaUAb66vASFl/J7ZH05paSmDIQ8wAGRnKxw7Vjx9eiVboouK0oG2AAoLO71+zU8OzlrB5RLeSlx5ufzdu6WJicjJAZeLnBxwOMjLQ3k5CgpQWoqiIsjKQk4OLBakpVFcfCYtbQ4QkJ/vfeiQ+5o1NUrzWVsoMIQpKSktW7bkvZaXl5eTk0tOTq50/aa4uHjWrFmLFy+uKh5jUlLSzZs3v19lXLhwoZGR0fdlEhIS3r9/36FDBw5HYdUqqePHJRYtKp84sUJCAkVFgjsr4MGDBwUFtkAzQpqlpi4qLCzkO7yTm9t4R8d+ubm5BgYGZWVlQJmODnR00LPntzJ9+2rcvfsI6Cwn96Bx42ZFgj2ZOlBcXCw+WdDs7GzV1IIJMZeWLigqCoiOLjE05P6+Wo3R08PZswgOlnB0lLa05KxaVc7lpty5c8fQ0LBVq1bVVCwpKeE7xJpovmEFBYWioldAOVDKZKbyJmZ+KlNYWPT8eU5JySIA+fmvb9261Z3f/PRdu3a+c+fQ06dPzcymqKmpfe1LSQlKSp+jGbRt69yrl2NxcYSExENv76Xic83zpqzEJGyskpKSkZH2jRtOKirMpk0VgoObrF9P3NzKx4/nyMt/C4A8fHj/XbvG5uVZMhjb1dT+FfaX6eY21s9vcXFxdosWBadOzZCTq6674mKUljIKC1FWhtOnNVeuvFtcDCBeTU25tjq5XG5NfmUUGEIZGZmyss9Pc4SQ8vJyWVnZX4uVlZUNHTrU2Nh4XtVBIVVVVXV1dZ2cnL4e0dfX/761Xbv+XbLk34qKDkzmP5KS562tNWJjCZstKYwTV1PrVl6+CejJYLxp1qyhXKUhSmuMrq5u9QWCgtYPGzY9OTm9d++uLi7jxcf2VPUPpYTcXBQVBUdHFzRvLnfkCNPennHhAvfHJyUBMHYshg0j/v4SZmbxpaXOHI5Tgwb7fHzGTZw4qppafBtC0XzDp041Z7Gc5eU7SUtLrFvnraCg8P2nL17g8GHGoUNyhYX5QDKgzGQ+bN58el2E6enp6VUbv0dPT+/ly1v37t0zNvZW529PqHDgcDiysrJiYggBaGntnTUrdvRoromJCYAHD8j69VJ+ftKTJpEZM4i2NgD4+PxjZXUlLu61tnawm5tey5akVy8h5gnw8vLQ1lbatWvXjRvhv71ZfX8RtWw57NWryEOH4po02Xj48IHaXmC8dbffl6vtlGvdmTFjhqurK+/1x48fGQxGTk7OT2XKysocHByGDh36/Xrhr1S/RpiRQRo27AIUAYTB2DNrVkDdxVdFZCTR0iKrVj0YPnyaquqys2ezhNfX94jVghwPsZLk40PGjv329tgxoq1NYmKE1d2ECcuBUwAB8nR0eiYmVllSzNcI16whhoYkOZmUlZV979qQlUV27iTm5kRbm3h4kJs3yY0bt1q27M5idXJy+lfYqniI1QXGQ6zWCN+/J+rq5Jd7Knn7lnh6EjabjBlDYmNJRUXF/v0HZ89e+ujRo5s3iYYGEXYSLXGOLEOBIbx69aqOjg7vavb39+/Tpw/v+J07d3ipsyoqKpydne3sfh+NKSgoSFnZZPXqzxYuKYmcOUO8vcmgQaRJE6KkRFisXkASQGRl1+zeLawQTGfOEDabnP8SMW3LFjJ8uJC6+hkxvCmIj6SyskrCgAUHEw0Nwtdv6vesX79VRmYdQIBoVdXBWlpETY1YWZGFC8mxYyQ+/nOxzZsDtbXb6ep2CA09x0cvwv6G164lhobk0ycye/ZydfV2bHbb9esDT58mjo5EUZE4OpLTp8lPD6gPHpBmzfhxGuID8bnAviJWhtDDg3h5Vflpbi7ZtIk0akQaNlwoKzsTOK2m1jk2NvbGDaKhQa5fF6Iw2hD+AJfLdXJyatGixcCBAzU0NCIjI3n0Z9nPAAAgAElEQVTHbW1tlyxZQgg5duwYgDZt2ph9oarQnUFBQXJy6XJydhYWzzQ1iYYGsbH5fNN5/ZpwueT27btaWiZstrmFhUPdgxxWCs+T/v79b0fy8oiqKklKEkZvPyOGNwXxkbRvH7G1reR4cDBp2LDKOJl1obi4uFevoWx2+xYtzF+9ekUI+fSJhIURb2/i4EAaNybKysTc/IOcXHegFMjW0DCpfs6jUoT6Dfv4kJYtyadPJD4+ns22BQhQxmC069u3+N9/K3Hq+0rnzuT0aeHp+ob4XGBfER9DmJlJ1NR+7wxfUkJUVTvyPEuBo97efoSQS5eIpia5d09Y2mhD+DNcLjcqKurcuXNZWd+mED98+MDzwM7Pz4//karOJCgoSEqqUFZ2obf3xWo2LAhvd6CfH2nalLx48fNxd3eyZImQ+vwBMbwpiIkkLpcYGZHw8Mo/PXGCNGxIoqJEq4mQ1FSyefODBg2m8hKIq6n1+f4nUEOE9w37+pKWLT/fRh8/fqysPI6nU129Z/av2/p+5PBh8mVyR7iIyQX2PeJjCFetIpMm1ahkz55DGYxwoExCYsLhw58fYS5eJJqaPzzWCxBxNoSV+4xkZGRcv349NjY2IyNDUlJSQ0Ojc+fO3bp1ayCgnJsMBqN9+/Y/HWz0JYKvvLz8rwERqkJKaquGRvicOYurqSEo2d9DCLy8cOYMbtxA48Y/f+rhge7dsWgRxMZr5K/j7FnIyqJPn8o/HTIEsrLo1w+nT6NTJ9Gp0tDA1Kltt26NfffOD8jNz5ePiVHp1Ut0Aqphwwbs2YOICPCcKcrK2hQWpsnLu8nI5Pfq1UJZWbn66sOGwdMTsbHg7XygET2lpdi2DZcv16jw4cObx42bEx+/WFt7sL+/vY0NVFVhbY39+2Fvj7AwdOggZLlixU+G8dKlSwMHDvzq1fP9Hj4Wi+Xi4vLkyRM+zLKQCAoK6t69x6++NsKmooJMmkQ6dybV7CG2siJBQUJXIoZPx2IiqUcPcuTIb8qcOyfcuaCqKCgo2LUr8MiRIxculGlqkgMHalddGN/w+vWkRYtv8/kPH5KGDUloKOfatWv3azxAWLWKuLgIXNrPiMkF9j1iMiLcvp04OPBT0dOTtG9PMjM/vw0JIWy24OdLxHlE+M0Qvn371srKSkJCwtraeseOHTExMV8X1fLy8m7duuXr69uuXTsJCQkXF5eCatYKRIjA8xHWhMJC0r8/GTCAVD/hevo06dxZ6GLE8KYgDpLu3ydNm/7s0FEp588TTU1y9y7Jzc397eyfAPnqNfr6NWnRgnh41CIFpsC/4Q0bSPPmP1hBTU1+FvzS04mqanVPhwJBHC6wnxAHQ8jhkObNya1bfFZfsIC0b0++ztOfOEG0tIhgRz3ibAi/bXyJjIzU19d/+/btxYsXp06d2rZt269jQQUFBXNz8wULFkRHR0dGRqakpHz8+FHEI1fKCQkJtbef5OW1zsqqRFUVISGofsK1f3+kp+PBA1Hpo/kOPz/MmVNJNI1fsbXFgQPo29enSRPrFi36T5++SPjqfsDAAHfu4PFjODmhuFh0/XK53FevXmVnZ2/ciO3bEREBHR0AuHsXdnYIDIS9fa3bVFfH4MHYvVvgYml+z8mTUFGBuTmf1X18YG4OK6vPKVSHDMHmzbCxQXj4uwMHDkRHRwtQqjjCh40VH0Q2Irxy5aqKSj/gMZO52th4zo+xUavE3/+HTWzCQAyfjimX9OYNYbOr82/8iZycHCWlLl9iM1q/e/dOmOo+89M+wpISMno06dKFpKT8vm7dv+Hi4mJT0z4aGk7y8h01NQ9/HQvevk00NMjFi/y3HBNDGjUi/O6QrBGUX2C/Ig4jwi5dSEhInVrgcombG+naleTmfj7i7x/LZLaXkAhQVbXZtauu+0Trx4iQphouXLiZne0KmHC5C/Pzb9UwdNrEiThzBmlpQhZH8yMbNmDqVNQ85mV5ebm0tBwvNmNFhTwlmWJkZPDvv7C2RrduiIsTenehoadfveqRlnakoOA6k7mBNxa8fRuDB+PgwTql92vbFi1a4MQJQSmlqRE3byI7Gw4OdWqEwcCWLWjXDv36IT8fAN6/P8rlruJwPLKyjgYE7BeEUjHl94bw9u3bo0ePtvqCCDSJIa1bd2Qyg4AkKaldnTr97O9aFaqqGDqUnikSKZmZOHIEbm61qKKurt63bys2e4iSknNRESs7u7nQ1FUHgwFvb3h7w9IS4eHC7Ss+nlFS8jnuFG8C+fZtDB2KQ4dQ95+4hwcCAuraCE2t8PPD3Lmoe4g3BgNbt8LUFLa2yM9HkyYNZWWfAgCeZGU1TE2ts1CxpfoBY3l5eatWrUJDQx9+gY/BqfAQzdTo+/dEX584Oe3t3Hng9Ole+fn5Na/75AnR0RHiTJEYThNRK2nZMvIlfl/teP78+ZMnT3i+Mw8eCFrWL1QTYu3OHaKlRbZurbJuXb7hrCzi4UG0tYubNOmroTGcze544MCRW7eIpia5fJnvVn+AwyH6+kL0xRXDa57aqdG4ONKwISkuFliDXC6ZPp2Ym5P09BIbmxGammZt2vSZNStRQ4Ns2sR//CBxnhr9jSHMy8sbM2YMH92LBhEYQp4V3LyZ/xa6dyfBwYIT9CNieFOgUFJhIdHQIHFxdWqEt6dC2I981ccajY8nrVpV6UrK3zfM5ZIDB4iWFvHwILm55KuzDC/OZFWRB/hj40YivN+lGF7z1BrCCRPImlrn6fsNX21hdnbF27dvi4uLCSEvX5K+fUm7duRLNLDaIc6G8DdjaQUFBRUVlXfvhJ6tSjz58AGWlvDwwIwZ/Dfi7o6tWwWniaZq9u2DhQUMDevUiJ0dtm2DvT2ePBGQrNqjp4fbt/H0KYYNE0yysJgYWFhgyxaEhiIgAIqKmDZtYY8eTsbG/R0cog8frjLyAH9MmoSLF5GUJMg2aSrl40ecOoUpUwTcLG+OtHnzdC0t886d5zdr1vXevcgWLXDpEmbNgoMDpk5Fbq6AO6WQ6gyhvr6+qqrqvn37mjZtqvoFkSmjHJ4VnDEDHh51amfwYLx5Q+Vd9S+Bw8HGjag6Z1ctGDIEmzbB1hbPnwugNf5QUcHFi9DURLdu+PCB/3ZyczFzJqysMHw4IiPRsSMAXL58+ejRzJSUqKSkwyzWLMFaQQAKChg1Cjt2CLhZml/ZvBnjxkFNTfAtMxjQ0tpdWjojLS04JeXEzJmreAfHjkVcHGRlYWSEf/8VfL+UUJ0hjI+Pz8rK4k1EZH1BZMqoJSkJvXvD3R0zZ9a1KSkpuLrSg0Khc/IktLQEliZ++HD4+8PaGi9fCqZBPpCUxPbtmDAB3bqh5vu4uFzurFlLDQy6OjlNO3o0r21bZGfj+XPMnPnNmeLly095eW0BBqBbUSGUjKzu7ggMREmJMNqm+UxeHvbuxezZwmqfw+EAvJSZUsnJFRzO5+PKyggIQGgotm6FpSX8/Y8bGHQzNu5z926ksKQIm5pPtlZUVPASMYsPQlojTE4mrVqRtWsF1mBaGlFR+RbBSICI4XoJVZK6dBF89oMjR4i2Nnn+XMDNklrmI+SljgoL+/y2+m9427ZAFmsuUMZg7FFTm/lTfDTeSiGbnSInZyopuUFZeZyb2yJ+TqAGDBhA9uwRfLNieM1TtUbo50eE6sLx6dOnJk3M2Ozxamrtzcyut2tH7tz5oUB5OVm2LInJ7AbkAe+1tU25VW+yrjdrhF26dPnf//731UCOGjXq3r17Xz89evSoMKJXixspKejdG2PGYOFCgbXJZqN/f+zfL7AGaX7i2jVkZ6N/fwE36+SE1atha4v4eAG3XCuGDcOZM5g6FZs34+3btxcuXEhPT/+pTEoKwsKwfDnWrn1eWDgQkCJkiLb2M95cKI/oaHTrhq1bcfas5rt3lwIDVY8fH7N162ohyZ45Exs3oibpwWn4oLwcW7dizhwhdqGlpfXy5e3w8NkJCREPH/bw9oazM8aOxderT1ISVlaJSkodAQWgcVqa6ogReQEBuH27krXthISE9PT0YlHGT6oxPxjC5OTkvLw83mtCyOHDhxMTEykQRR2pqZ+toJeXgFt2d8eWLfg6t0AjWPz9MX++APZR/cr48Vi2DL17IyFB8I3XnE6dEBmJgICw1q1HT5jwtE0b68jI57duISAAY8fCyAiGhvDxQXY2nJ3tFBVXAZcVFOY6OX1+NMjKwsyZGDgQ06Z9Xilks9njxo3rI/Dlwe/o2xdMJq5dE14PfzUHD6J1a5iaCrcXGRmZtm3bKioqArC3R2wsVFRgZISAgM93M1NTUwWFW0xmkIxMQMuWkv36KSUkwNMTmpowMsLYsQgIwK1bmDt31eLFh2Njc9u06fXVyogPNYjG+Bfw9OnThw8fNm/eZcoUw9GjBW8FAXTuDA0NXLgg+FELzYsXePRIiNFMJk4ElwtLy7KRI3cmJr6cMGGQtXVfYXVWNTo6UFLakZBwDNApLLTo0WO/hYVfhw6wt4e3N5o1+1qwr40N8/jxyz162Dg7O3K5OHgQCxfC0RHPnkFJSaSa3d0REABLS5F2+jdACNavx+bNou5XSQkBAZg4EW5uCArCtm3o1IkVGRm2c+e/ysoKLi4hLBbGjgWA8nI8eYIHD/DwIfbvR0zMSUJWANuSky0uXrzo6OgoaunVQhtCnDlzfsIEn5wcR2Di2LFrFi3qJaSO3NywZQttCAWPry88PISb+tHFBXv2ePn6KhAy8tKlJWfPynfp0kWI/VWBhoYy8AHQkZR8P2+e8uoqJjVfvUp8/fodm61871757NnSMjK4eBHGxqLVCgAYPRr//IP4eOjrU9D7H8zZs5CURO/e1PRuYoKbNxEUBAcHWFnB2Tk+IuKhggKrf38rAwMDXhkpKbRvj/btMXUqAOjqSnz4UAhARua9srLYpTqkY40iICAoM3MXh+PO4WxPTw8SXkdOTnj8GK9eCa+Hv44HDx74+Ow8dSqO92MTKsnJNwlZBnTLynI/c+aq0PurjP/9z9vAYA6b3cXUNNjT073SMocPBy9cePXKleVr1hRbW6/28MC1a9RYQQBycpg4kXaZFiQ5OTmhoaHe3k8E6MTAB7x9FM+eQUIiw95+1vXrK8LCXKytR1VVfteuNaqqC6Wlb9vYoG9fCiZUqudvN4SZmUhKagg8ASApGdusmZbw+pKRwaRJ+OKNRFNX9u//z9Z2iZeXRGnpxGfP7gi7OxMTIwmJQ0AGk3n02rX2798Lu8NK0NfXv3v3dFCQ97VrJ3jLNl8pL8fjx9izBytW3M3Lmwq04HBm6+reHjkSNYwRLyR4c2i8IM40dSQlJaVNG8sRIx49euT59i31zxeqqpg06YW8vAVgCJh//Kg4d25OSAh+DcFia9t33bol7doZHjy4hUHtFVkp37uQ6urqMhgM5hcAfP+Wp54PB1bhUZftExwO2b2baGqSyZPTO3Tor6lpZmHhIOxk9x8+EDU1Uptgpb9BDF3JRSapY8cBwCeAALdHjpwh7O6ysrJGj55hamrj779jxQqirk58ffmPIlur7RNfuXIlQkOjg6qqm7a26cuX8dHRJDCQTJtGOnUiDRqQ1q3JmDFkzJhjLNYo4LWs7LJZs5byqU+gODnVKUjhT4jhNS+y7RNbtmxnMncCBCjT1e0kgh5/S3Z2tpaWGXCDwTjZqJG5tzcZOJDo6BA2m9jaksWLSUgIef+erF27WVGxrZRUYyOjHnxswxP29okf1giHDx+ekZFBjUEWLY8eYfp0MJm4cAGmpupAmGj6bdQIlpY4cIBMnw5xfCyqVzRr1ujhw4eE2MvIPGjevJGwu1NRUQkK+uacMGoUZszAv/9i2zb06CHszj/j5bUxLe0Y0Aw4a2QU2LLlmvbtYWaGkSPRrt3XzFOOXbvmBwcvsbAw/ecfoe21rg0eHsTR8UxRUcLw4Q7NvvPqoakt6uoqMjIxxcUAUlgsOarlAICysvKlS/tXrtymoqKwbFmw1pc5teRkREUhKgr79sHNDamp+7ncVcCOxMRO58+fHzJkCKWqf4EPGys+8DEizM4m7u6kYUOyZw+pYX5dwTJ16v8kJNqy2W39/bfVvTUxfDoWmaT4+FQpqQFstpmt7SiqQj2EhBBdXTJ2LElNrV3FWo0IORwSEUFcXYmU1HAgBiBM5n8eHstrLZci3Ny8JCSmMBj7NTTaJSQk1LE1MbzmRTYiLC8v19EZzWKZ6ep2uH9f+ElSBIeWlhlwAuinqDgn7GtsiBpDJ+YVABUVFQAIwb//onVrVFTg2TNMnEjB2klWVtaJE0EcTnR6erSf37/Z2dmiVvAHERys4eh4Ji3t4fnzB+XkqHk6HjwYz59DUxPGxti+HVwuAOTk5HB5r+oGIbh9GzNnolEjzJsHPT2EhS3R0RmvodHPwGDzkiW1SbpIKaGhlzmcHYSMS0/3CA09R7Wceszbt5Ll5UGfPj149+5Bx45i53tZDRs2LFJSmictHdmpU5KtrS3Vcn7m29TomzdvlJSU2Gz2b+vExcU1bNhQRUVFmMIEw4MHD4cOdS0tlWzatLG09KGSEunQUHwfa0PE5OXlMZkNAQkADEbD3NzcevE1iiGFhdi0CVeuUK0DYLHg54exY+Hmhj178nJzHfLzJSUkMk6f3mNmVtMczj/x4AGOHkVwMBQV4eSE69fR/HO24Dbv3j1ITEzUr1fbETQ01JKSYoC2DRrcatFCzObE6hVr18LNDYqK9W9Vxdl5iJycREBAwOXLR6nWUgnfRoQxMTF6enrz589/9uxZpUUJIdevXx85cqSpqWluPcnAMWGC54cPIWlp9x48MGnW7L+v0fepokmTJi1bEmVldybTXUcHTZo0oVJNfWbnTnTvjtatqdbxhTZtcO0amjYNjI8fkZp6+dOn4KlTl9a2kdhYLF4MAwOMGQMWC+fP48kT/PPPVysIABISEhoaGoKULnyOHt3avv1CFZXOmpra/fr1o1pOfSUxEWfO1DUZDoVISkpKS0tTraJyvo0Ihw4dymKxFixYsG7dOkNDwy5durRo0UJVVbWioiIrKysmJubu3bufPn2ytbWNiopq2rQpdZp/ICYmMS4urlWrVry3ZWV48gQPH35ep33+vAjQBMBgNGrTJkcYIbhqBYPBuHYtJCIiIiyM8fp1L9pfhj+Ki7F+Pc6fp1rHjzAYaN68CGgMAFB+9KjYyAhmZujQAR06wNQUXyP1xsfHr127XV5ebvFiDzabHReHo0dx7BhKSuDkhOPHhR43S8QYGBhERV0oKYGBAWJiYGJCtaD6iY8Ppk6FsjLVOv5EfvAatbW1tbGxuXbt2oEDB65evbr/S5RoBoNhZGQ0fPhwFxcXIyMjCmRWzdu383v0GLN8+YVXr9SjohATA11dmJnBzAxjx+LBA5fVqweUlXVVVAwbM+Ys1WIBgMlk9unTx8ICBgZ4+BAd6tM8v7iwezc6d0bbtlTr+IXp08cFBTkUF99hMu/u3PlPy5afH8iOHft2ZbZpU+DvPzwzcyWDkfvff0OaN7/57h2GDMGuXTA3p3jPn1CRlcWcOVizBkfFcW5M3ElKQnAwXrygWscfys8h1hgMhqWlpaWlJYDc3NzU1FQZGRk2my22eSdKSgaUlESFhj4cONDW2RmmpvjebcLCYpKdnXl8fHz37vN+2oBMLTIymD8fa9YgJIRqKfWN0lL4+yM0lGodldG4ceMXL25ER0c3b75AS0sL+Bx3GF9CL0ZF4dy5Zzk5XQnpRwgKCg54eWXY2alTPlchGqZOhZ8fXryAoSHVUuob/v6YNAk1cOGg4YfqYo0qKSkpiThGb+2Rk3smJ3c1MHCyrm7lBQwNDQ3F8mc3eTJ8fREbK44jG3Fm3z6YmKA9n24oQkdeXr5HZfsKv4ZeHDLEoHXr+2lp74BcVdX0fv3U/uBR4E+wWPDwwNq1OHCAain1ipQUHDyI58+p1vHnUu8fRJWUXPbu/Ue3KjMoxsjJYc4cVBU3maZSysvh64tFi6jWUQfU1NT++8+vfXvXHj1Wnj9/4G9bJ3Z3x7lzePOGah31inXrMGYMNDWp1vHnUu+zT/Tq1cLe3o5qFXzi6gp/fzx9ijZtqJZSTzhwAC1bols3qnXUjd69e92+3UVCQkJKSopqLaJGURHTpmHdOuzYQbWUekJmJi+NEdU6/mjq/YiwXsNiYdYs+PhQraOewOHAzw+LF1Otg6ZuzJqF4GBQErW8PrJhA4YPh44O1Tr+aGhDSDHu7ggPp3Mz1YiDB9G4Mbp3p1oHTd1QVcXEidiwgWod9YHcXOzejQULqNbxp0MbQoqRl4ebGz0o/D0cDtauxZIlVOugEQTz5uHff5GcTLUOsWfTJtjbQ2y2bf+x0IaQejw8EBaGt2+p1iHeHD0KNTX06kW1DhpBoKmJUaOwefPvS/7NFBRg+3Z4elKt4y+ANoTUo6QEV1d6UFgdXC7WrsXy5VTroBEcCxZg1y78HWnf+GTLFlhZoUULqnX8BVBmCLOyspKSkqjqXdyYNQvHj1eS1pmGx/HjkJeHlRXVOmgER+PGGDwY//sf1TrElaIiBATQw0ERQYEhJIS4u7s3b97cwsKic+fO6enpvxYYPHhw48aNGQzG7du3Ra9Q9KiqYsoU+PtTrUMsIQSrV2NprUNY04g7Xl7Ytg35+VTrEEt27EDPnvTGKhHxzRC+fPkyODhYBF1eunTp9OnTr169SkxM1NPTW7ly5U8FGAzGwIEDz50791elKJo3D0eO4ONHqnWIH6dOQVIS4pfCjKau6OvDygrbtlGtQ/woKcGGDfROIdHxzRDeuXNn+ZdFmIYNG965c0dIXR4+fHjEiBFqamoA3NzcDh069GuZCRMmGBsb/1VBN9TUMH481q2jWof4sWYNli37k6NR/80sXoxNm1BURLUOMSMwEB060MEXRcc3Q6iiopKVlSWQzNrVk5iYaGBgwHttYGCQlZWVz+/kCIfDycrKivpCTExMeXm54JSKmvnzceAA7VP+A2FhKC+HvT3VOmiEQ6tW6NYNgYFU6xAnysuxbh09HBQp30KsdejQIScnx9raWl9fPy8vz8/PT7Oy2HY7d+6sY5f5+flyXzJEsFgsALm5uQoKCnw0lZiYGBkZ6eLi8vXI+vXrO1Kbe7cONGgAJycZX1+sXFlawyoFBQVClcQHgpW0alWDefPKCgoqBNimOFBSUsJ3iDUx/KfXhTlzmM7ODUaOLJCRqVF5MTz9oqKiiooKpoByiOzbJ9WihaShYfEftnpaXFxcUVHB37CHy+UWFRXxUZfL5crIyPz2h/bNEDZq1Cg4ONjPz+/MmTOlpaVXr16VlKwkEmndDaGmpmZOTg7vdVZWFoPB4Dvjtr6+vp2dXaWTq/WUpUthbIzFi6Vrnm+Fv2cIoSIQSdeuXYuIyMnLsxk5Uu7Py1IkJSVVl1ijYvhP5xtzc7Rti5AQhSlTalpF3E6fyWTKycnV3RByOJyUlPQtWxru2yd251h35OTkJCUl+TsvJpPZoEEDPupyuVwOh/P79r9/M2DAgBs3bnz69InNZl+4cCGrMmqr41dMTEwiIyN5ryMjI1u3bi0tLV33Zv8MtLQwfDg2baJaB9WMGDF9yJCg1asf5eT0KS0tploOjXBZsgS+vqj404b9tePJkydNmnRs3drl06duhob0/kqRUvkjzO7du1u2bFlNtTdv3uzgN3r85MmTQ0NDDx48ePfu3SVLlri7u/OODxs27MiRI7zXFy9eDA4OLisri4iICA4OFsPJEOHh6YmdOyGIR476SkVFxdWrUdnZezgc7+JiC+H5bdGICV26oGlTHD5MtQ5K8fBY9fHj/ry8sJISDx8fen+lSKncENrb2/O8Oqvi5cuXm/gdtujr64eGhh45csTT03P69OlTp07lHTc0NGR/mRDkGUI7O7vY2Ni/zRA2aYLBg7F2bcbr16+p1kINkpKSTGYpkAcQCYnX6urqVCuiETqLF2PlyuTLl8MzMzOp1kINxcUlgAIALlehqKiEajl/F9TkI7S0tLS0tPzp4KpVq76+3vB3h6bX0Njp43PgwAHtli3LIyJOVLpY+2czcuSaLVt6Kitzx4wZamJiQrUcGqHDYFx/927+oEF9FBTmh4cHtfn7dpJ7e8/p12+4gkIXFuvW/PkhVMv5u/jjnBD+CPbs2cHlXk9PP/70abNr165RLUfUlJXh1Kl+ly8/SkuLWb+ejijzV+Dtva28/GBR0drU1AAfn91Uy6GA6OieDg4Xzp8f+erVrWbNmlEt5+/irxtq1C/KykAIoVqFqNm+HUZG6NmTah00IkRJSR5IA1owGKksljzVckRNRgY2bsTdu2r6+l2p1vI3QhtCcWThwmlr1vTgcrULCjjJyb2pliNScnKwdi2uXKFaB41o2bhx8ZMnIwoLZSsqSGTkicxMVOul8KexciVGjoS+PtU6/lZoQyiOzJo1ZcyYodnZ2RyOQZ8+kJTEyJFUaxIVPj5wcICREdU6aESLnp5eQkJkYWEhi8VasgQ9eiA8HFpaVMsSCW/f4vBhPHtGtY6/GNoQiilqamo8x93wcPTtCyYTzs5UaxI+Hz8iMBCPH1Otg4YieKGmVq6EvDx698bly2jUiGpNwsfLC3PmgN+wIjQCgE9DyGQy/0JXRkowNMS5c7CxgZwcHByoViNkFi+Gm9tfce+jqR5PTzCZ6N4d4eF/+IThgwe4dQt791Kt4++mFsasqKiIw+HwgtzY2dnZ2dkJTRXND7Rti/PnYWcHJvNPDj8dG4uLF/HyJdU6aMSD+fOhoIA+fXD5Mpo3p1qN0Fi4ECtWoEEDqnX83VS+fcLZ2Tnwl4Dwx48f19fX/wudGMUBU1OcO4fJk3H2LNVShMaCBViyBIqKVOugERtcXbF6NSwtERtLtRThEBaG5GSMHUu1jr+eSgxheXn5yZMndXR0ALx///78+Ry9b24AACAASURBVPO841ZWVunp6YmJiaLUR/OVdu1w6hTGj8eXf8gfxbVrePsWkydTrYNGzBg1CuvXw8oK9+9TLUXQcDjw8oK/P+hVJsqpxBBmZGSUlZU1btwYwM2bN2fOnMk7rqGhwWAwMjLoaLCU0aULQkMxfjwiIqiWIlAIwbx5WLsW/CZjoPmTcXLC3r0YOBB/WNDZ/fuhpob+/anWQVOpIeTlguBlfsrMzPyacSItLY0QUn0MUhph060bQkLg7Iw/KeDMoUOQlMTgwVTroBFX+vfHf/9hyBAsWxY6evQsX9/N9ToFN4DiYnh7w8eHah2iIjc3d+/e/2Jint+9e49qLZVQiSFUU1PT1tZet27dkydPAgMDJSUljx07BmDTpk1qamqNaJc+qjE3x/HjGD48u0+f6Z062fv4bK6mcH5+/vXr1z9+/CgyebWlrAzLl2PdOjAYVEuhEWMsLTFv3qWVKwNPn56+YkXa3LneVZV8/vz5yJHurq6enz59EqXCWrFxI8zN0aUL1TpExcCBE0ND1VNSGjk4zE5ISKhV3U2bdr54Eb9gwer09HQhyat8cnrVqlUuLi4hISFWVlYeHh5OTk6urq7Z2dn+/v507kBxoHt3GBjMvXrVCvBZs2aGoeHpQYMG/lrsw4cPXbs6FBVZSkjc2bbN09FxkOil/pYtW2BsDAsLqnXQiD2JidcJmQGYFRW12b27z5s3UFf//Kep+fmFjEzugAFjUlM3MBg5V68Oe/VKHKdTMzKwaRPu3qVahwh59eo9hzMJeJ2RYWVufldHR09eHrKyUFQEiwVZWSgpfXvRoAFkZaGsDDk5xMaGL116paREPyLCfOTIGZcvHxGGvMoN4YQJE7p27fr+/XtLS0spKSllZeVHjx5169atPz2fLTakpDwHdgMS+fnDBg9+1KDBQBkZKCtDWhry8mCxIC2NpKSDHz96Ak5A9uLFQ8TQEGZnw88PN25QrYOmPtCzp1lQ0MG8vLZSUsd79zZzc0NGBjIykJ6O+PjPr5OSnqelmQM9CcGnT9tPn860tVUTt6f3VaswYsQfvj/yJ3R01FNTHxJSrKx8OjBwS8OGyM9HSQny81FQgJIS5OWhsBB5eXjzBoWFKClBbi6KivDuXWx+/mBgPSG9Xr4UihVENfsIDQ0NDQ0Nea+HDRs2bNgwISmg4Q9bW8sDBxYXFfVTVd0YFrbaxASlpcjJQVkZCgpQWIiyMhw4wHrzJrOiAkD227dybdrAygrW1ujRAywW1ScAAFizBkOGoNok0DQ0n3F0HPLhQ+rBgxO6dDH1919T6TWcmdnCyOhuamockCspmblmjerYsejVCzY2sLGBnt63kunp6enp6YaGhkymSJPwxMf/jQHVTp/e4+Tk8uTJs717A/v3r0UExcePe/ftOyMzM19GZpelZTdh6SP1maCgoJEjR1KtghrKy8s3b945YoTblSsRVZXJz883Ne2joWGpo2P68GHU06fEx4f07UtYLGJmRjw9yc2bhMMh27fv19Fp36RJx5CQsLoLy8vLq2HJxESipkaSk+veZ/2juLi4rKyMv7o1/4b/SH57+rdu3e7RY1j//uNevnxJCMnIIMeOkSlTSOPGRE+PTJlCjh0jGzfuZ7O7sNkjTE37FBcX11FSQUEBh8OpYWEnJ7JmTR07rJeEhYXZ2NjwUfHmzVuqqhpz5iwsLS2tbV0Oh1OTHxptCOs3Nbkn5ubmcrnc748UFJCzZ8msWaR1a6Km9klGxhwoAXI0NEz4vjvXShKPMWPI8uV17K2+QhtCvqnL6cfEEF9f0rs3YTDMgBKAsFhLT5w4UUdJNTeE9++TRo1IYWEdO6yX8G0ICSGmpqYPHz7ko2INDSGdmPfPR1FRkfGjRyaLhX79sHEjnj1DUFCytHQrQAZQKi3VysnJEY2qR49w5QrmzhVNbzQ0ANC2LRYswJUrvLwWXAAlJRXp6aK7DS5YgOXL6YBqYgdtCP92+vY11tJ6JiPjLyu7rKxMYuJEdlKScHvcvDmwWbPOPXtaTZjwWP6vy8BKIxasWTOPze6hoTFIV/fxP//09/UFlyvE7nJzc729/e3tF3/4ED9unBA7ouEP2hD+7UhJSUVHX96+XWPfvtYZGSctLGBmhl27IKSYss+ePVux4nhi4vX8/B2HD08TSh80NL9j3DjnN2+u3L8fEB8fdu+e1NmzsLHB+/fC6q53b8fVq5XDwrpkZw/Pz88WVjc0/PIbQ1hYWBgYGJiZmSkaNTSUwGKxJkwY5+zs1KCBlKcnwsMRGAgrK9Ry22uNiI+PLy3tBsgC+oWFHK5Qn8NpaKpGUVGxSZMmDAZDXx8REejdGx06CCUdUklJSVJScXn5ZMCey7WOiooSfB80deM3hjAzM3Py5MnvhfekRCN+GBvj3j0MH44uXeDrCw5HkI2bm5szGKcYjINycitMTfVF7LlOQ1MpEhLw8kJEBLZvh60tBLs6ICsrKy1dCjwF0qWkbrakdwuJH/RtiKYSmExMmYLISISHo2NHPHoksJYzMtSkpU/PnZu8ebPumTMHBNYuDU2dMTLC3buwtET79ti1S5Att2y5T1d3SZs2zrt2LeTlM6ARK+j8HzRV0qwZLl1CUBDs7GBj8/D27ZkFBcUDB/bZudOPwVdgUC4XLi5YtaqRq+t8gauloak7kpLw9ES/fhg3DiEhhRUV0168eG5k1OLYse1KSkr8tXnkCNLSjF6/PiluAW5ovvKbEaGkpKSWlpYUnR3nb4XBwNixiI5GSMjs+Pj/UlOjjx3LvXz5Mn+t7dgBAFOmCFIhDY3A4a0O5Oauv3q168ePD69etVm4cC1/TWVmYs4cBAaCtoLizG9GhNra2uIcwZ1GNGhrQ1GxuKCgMYDCQuOPH/m5JD5+xPLluH4d9LIgjfgjLQ119URCBgGoqLCIizvHXzuzZ2PECHTqJFBxNIKGvifR1IihQ22VlcdJSGwgZF+7dv34aMHdHW5uaNVK4NJoaITClCmOqqqzgIMMxnQLCyc+Wjh/HrduwbvKhFE04gK9RkhTIzZvXjV4cERSUtKrV5cmTVK/caN2YbuPH0dcHI4IK3Y8DY3gsbe3u3BBPSLitqKit7d3lzFjahcdvrAQ7u7YuRN01AjxhzaENDXF0tKS9yIjA05OCA2FhESNKubmYs4cHD4MGRkhyqOhETgdO3bs2LEjAFlZ9OuHu3ehoVHTup6e6N0bVlZClEcjKOipUZpas3kzSkvh6VnT8vPnw96eTr1LU48ZPx4jRmDIEJSU1Kj83bs4dQp+fkKWRSMgaENIU2ukpHDiBC5exLZtvy984wbOncPq1cKXRUMjTFauRLNmGDv299EHS0vh4oItW6CiIhJlNHWmRoYwKSnp3LlzHz9+FLYamvqCoiJOn8aqVQgLq65YaSlcXbF1K5SVRaWMhkY4MBjYswfp6Vi69DclV6yAkREGDxaJLBpBULkhdHJyWr58Oe/1tWvXWrRo0b9/f319/VOnTolOGo1406wZQkPh4oLY2CrLrFqF1q0xaJAIZdHQCA1paRw/jqNHq4s7ExuLPXuwZYsIZdHUmUoMYUVFRWhoaLdu3Xhvvby8mjdvfvPmzVGjRs2cOZMj2NCTNPWZjh2xZQsGDUJqaiWfPn2KHTsQECByWTQ0QkNNDefPw9sbV65U8mlFBSZOhK8vNDVFroymDlRiCLOyskpLS/X19QGkpaXdv3/f09PTwsJi1apV79+/pwNw03yPoyMmTED//igs/OE4lwtXV6xZAx0dipTR0AgHfX0cOYIRI/D06c8frV8PJSWMHUuFLJo6UIkh5AVUKy0tBXD+/HlCSJ8+fQCoqqoCoFMy0fzEP//AyAjjxv2Q2vR//4OEBFxcqJNFQyM0unfH5s1wcPhhLuT1a/j7Y/du8BWIl4ZKKjGEKioqOjo6gYGBubm5gYGB7dq109TUBPDu3TsAbDa77r2eOnVq3LhxM2bMePHiRaUF4uLiZsyYMW7cuNOnT9e9OxqhwmAgMBDZ2Vi8+PORT5+wahV27KDvCDR/LM7OGD0a9vYoKgIAQjBtGpYuhZ4e1cpoak/lzjKrVq0KCAhQVla+c+fO4i+3t7CwMDabraurW8cujx075urqam1tra6ubmFhkfrL+lJKSoqFhQWbzba2tp4yZcrx48fr2CONsJGSQnAwQkIwYMD2Dh0G9Oixx82NS0dTo/mzWb4choawto7s0KGfsfGMnJx8NzeqNf2J7Ny5Oy7uzYIFy4p4Dx01JiMjo39/p+vXf59GrvLIMuPHjzczM3v06JGpqWnbtm15B7W1tTdv3sxf/p3v8ff3X7t27ahRowBER0fv2bNn0aJF3xcIDAzs3r370qVLAZSWlvr7+w8bNqyOndIIG1VVdO26/MCBR8AyYG9MzG1ACNm+aWjEBgYDbm6Pu3SZCvgArxQUuktIPKZa1J9GcHDwtGmbCdG+elXdxMTq9evbNa/bokWP7OyJ9vZN+/b9TckqQ6wZGxsbGxt/f8TZ2bnmCqqivLw8Ojq6Z8+evLe9evWKiIj4qcy9e/f6fhHes2fPyZMnV1RUSErS0eDEnUuXzgE7gXZA88uXbamWQ0MjdI4fP8RgDCPEFrAtLKQ9pAXPnj3/EeIFbABmvHkzuVbZrMrL5YB5mzeT6dN/U7JK65KUlBQYGPjs2bPi4uKwsDAAYWFhCgoKX20Yf6SlpXG5XDU1Nd5bNpudnJz8U5mUlJSvBdTV1blcbmpqqk5l3ocfPnw4rnY8eEnw1yOysrISNYyA+UdACKn7GF1QFEwsREU3QA4oKZIoV/Gh42oIBbH6p4sesTr9csVysqAEWA9wuCikr/lqKC8vL+1QWtuvqLRrKUzDAC7QncEok2HVonp5QR6gOLzxNaB99SUrN4QPHjywtrZmMBiNGzf+6ib69OnTAwcOxMXF1VzHr8jJyQEoKyvjvS0pKWnQoMGvZb4W4Dmv/lqGh7q6uvYzbZMWJry3DAajadOm0n9TBsyysjLxOd/Tpzlv3waXl+cwGKp9+gw1NZWlWpH4UlFRwWQymXzlZhSrf7roEavTf/qUcSXyakXFKwZDRkenv1OfurpQ/MEkJCRERUU59nSsbcUTJ8Li41/KycmNGDFCXV295hXfvXt36lQ4S/c5n4bQ1dW1Xbt2J0+efPTo0ejRo3kHBwwY4OXllZaWplHzAOy/oKKi0qBBg/fv3/O8T9+/f//rUE9HR+frbsX379+zWCzlKiJ0ycnJdSPdDrkf4ltPfSc/P19BQYFqFQCwciVYd5F1Yx2Hk5+QoGBri38f09uKq6SkpERCQoK3Vam2iM8/nRLE5/Rv3cK+tYi+hiZNCgG5du2Y3ezh4EC1LHHl7Nmz+Sfzfa18a1vR18q3Xbt2gYGBZmZmta3LncitSRCYSh5Is7Ozo6OjV65cqaSk9P0URJMmTQDUMeIog8EYMmRIUFAQgOLi4uDg4KFDhwIoKir677//iouLAQwdOjQ4OJj3OigoaOjQoeIzE0JTKadOYedOhIaCN3Q3McG4cZg1i2pZNDRCIzERjo7Yvx+tWwOAnBx274abG3JyqFZGU3sqMYS82chfn7mys7MB1N1pZdmyZSEhIb17927Xrl3z5s0dHBwAZGZmjhw5MisrC8DgwYP19PTat2/fu3fvU6dOLf1tjFsaSomJweTJCAn5IYiMtzdiYkDHpqX5IykowMCBWLQI/fp9O9izJ+ztsXAhdbJo+KUSq6ahocFms8+dO9e2bdvvh2JHjhxhsVgtWrSoY5cGBgYvX758+PChgoKCqakp76C2tnZiYmLDhg0BSEpKnjlzJiYmJj8/v2PHjrKy9FKT+JKZiaFDsXkzOnX64biMDPbswfDh6NmTTkZD80fB5WLUKHTogBkzfv7I1xfGxggPx2/99WnEikoMIZPJnD179vLlyzkcjo6ODpfLff78+dGjR318fGbPni0jiCzjcnJy3bt3//6IhIQEb+qVB4PB+GojacSW8nIMHYqRIzFiRCWfdu2KgQPh6VldqH4amnrHokXIykJwcCUfKSpixw5MmYInT8BiiVwZDb9UPs/p6emZkZGxfPnyiooKAEZGRgwGY9y4cStWrBCtPBqxxs0Nqqr4krCrEnx80KYNLl+GlZXoVNHQCI+gIBw7hshIVOW4ameHbt2wbBnWrROtMpo6ULkhZDKZ69evnzlzZkREREpKirKyco8ePVrRIbNovmPDBty/j9u3Uc0WAAUF7NiBqVMRGwt5eRGKo6ERAlFRmDcP4eGoPuJyQACMjTFkCL7ksqMRd6rzfNHV1R03bpzIpNDUIy5dgp8f7tz5/fyPnR3MzbF8Of2ATFO/+fR/9s4yLqqtC+PPDA1KN15EEMVCygJFxUYRFLHAAsRur9iBQYiBjdhgYQeiomBhXMTuqxggIN01sd8P4+s1BpgZZuYMOv+fH8ZzdjyHOXPWPnuvvVY6Bg3Crl34MeIWF7S0sGEDfH3x8CGEsZQkReRwN4QfP36sbu+FqTS4+h/P69fw8sKxY7wG2t+4UTpAllK/KS+HmxumTYOLC0/lhw3D0aMIDKxp4UCK5MDdEHbo0OHXpBAcCCGi1CNF0snPh4sLVq8G77H2OANkHx88eiQdIEupfxACb29YWODvv/motXUr2raFqyusrUWmTIqQ4G4IIyIiKioqvv23uLj4+vXrZ86cWbNmjbiESZEsCgoKFi8Oefs2LS9vlItLr/Hj+asuHSBLqXewWKxVqzbExt6Ul+/IYPx97Rp/W6gNDLBmDSZMwJ07+JPiH9dLuH+1Lr+8/3t7ewcFBUVHR0+uNY63lN8RNzffW7cGslgj5OSmRUQYAK35bWHrVlhbY8gQtOa7qhQpFLBhw461a9NLS7fTaFtnzdqgoMDP+yAAwMcHx45h40bMmSMKgVKEBh8xfz09Pa9fv56SkiI6NVIkljdvPrJYo4E2LNbwpKR7ArRgYIBVq+DjAx4i/0mRQj3x8UmlpT6AISHjk5L+EaAFGg3btyM4GG/fCl2dFGHChyEsLCwEwG+OYCm/AYRARsaYRjsEvNTQONqhQ/va63DDxwdqagiTZm2TUh/Q0upKp68HnjZosNbFpZtgjZiaYuFC+PpC6lwhyfDkNcpgMN69e7ds2TJ1dfW6h1iTUr+oqsLo0TAxiejZMygtLWbmTP82tfqPVwONhp07YWPz4Pr1febmmosWzdCQhl+TInkQghUrcPfu2OXLce/e+r597SdP9hG4tenTcehQur19iK5u8ZIlk+zs7IQoVYpQ4MNr1NDQMDIyUnIygUkRAyUlcHeHigri4jQVFUPq3qCc3GcGY+LZs6Gysu9v3vS6dy+m7m1KkSJEqqrg7Y1373D7Nk1HZxwwro4N0unIzR2ekjIP0L13b9KTJzF1yWQnRRTw5DUqKyvbqFGjNm3aSONf/1FkZqJfP3TujLCwmsLH8MX9+/cJGQg4MpmOHz5sY7PZgiWnlSJFFBQUYPBgaGoiIQHCetpVVFSUlBBgAIDKyp6PHz/uJQ05KGHw6jUq5U8jJQV9+2LwYAQFCbPZNm3aKCmFlpZ6AR80NJSkVlCK5JCejv794eiIDRuENvIDoKioqK5OsrMvE2IAxFtaShN1ShzSx5AULiQno3NnzJ0rZCsIwNTUdM+ehR06zDAxOdCp034hty5FiqA8e4ZOneDqKsz5j2/ExR0aOvS0rW1wgwYb1dX1hNy6lDrz3xthbGxsEA+PvevXr4tSjxTqiY/H8OHYtg1DhoikfReXfi4u/fLz0bIlHj9G27Yi6UWKFN6Jj8eIEdi4kXtCsbpjbGx85Mg2AIMGYf16LFggkl6kCMx/hlBOTq6BNEHAH8/Jk5gyBSdO4Md8kcJHQwMrVmDKFNy8ie/SP0uRIm4iIzF3LqKj+YgaKDBhYbC1xciR+C77qhTq+c8Q9uzZs6c0rfIfSWZm5tate5WVFWVlvcPC1C5eFNNbmq8vdu/GoUPw9BRHd1KkfOPVq1eenjNzcnLbtPF6/HhGXBwsLcXRr7Expk7F338jOloc3UnhEf6i50n5/SgvL+/QYUBa2iygRE5u4Js3142NxdQ1nY5Nm+DuDhcXqKqKqVMpUgAMHjzx5cutQLPPn4efPt3e0rKT2Lr290erVrh6FT16iK1PKbVQkyHMyspKSUkpKSn5/qD0rfE348WLF8XFdmy2JwBV1XMKCl8A8S3md+iAvn0RECDNVihFfLDZyMwsBVoBoNMd8/LeAuIzhIqK2LgR06bh8WPIyYmtWyk1wd0QZmVljRw58urVq7+ekqZh+p34+BGhoY0LCx8CuUCZjEyatra2mDUEBqJ1a3h7o2VLMfcs5U/k/HksXgw63VZR0b+yso2W1v6ePc+LWYOLC8LDERaGuXPF3LMU7nB3E/bz83v+/PnBgwcHDBjg7e198eLF6dOnq6urR0ZGilmfFBGRk4P582Fri8aNtaOiljVvPsjS0vvEiR0yYk8Yo6ODJUswbZqYu5Xyx5GYiG7dMH8+Fi1CZubWnTvbBgRk3rlz0tDQUPxiwsIQEoLPn8XfsxQucHkjZLPZly9f3rVr18iRI+Pi4vT19fv06dOnTx9TU9OgoCBPT0+a1MmvPlNSgq1bERqKwYPx7Bn09QE4jxjhTKGkSZOwezeOHYOHB4UqpPy2/PMPVq7Es2dYsAA+PpzsgDKjRo2kUJKZGfz84O+PqCgKVUj5Cpc3wuzs7PLycltbWwAKCgpFRUWc46NGjXr+/PlbaUKR+gaDweB8qKrCzp0wN0dyMu7dQ3g4xwpSj4wMtmzBnDn4cT1aihRBKCoq2rVrz+HDRxgMxosXGDoUQ4bAxQX//gs/PwnKkbtoERITce0a1TqkcDWE6urqNBqNk3TJyMjo1atXnOPl5eWQpmGqV5SVldnbuzRq5Ghi0n7NmmfNm+PYMVy6hOhomJpSLe5HHBzQtSsCA6nWIaWeU1lZaWPTe8qUIh+fl8bG7k5OsLXF69fw84OshPnIKykhNBRTp+L/I1UplMHFECooKLRq1SopKQlAv379EhISQkJCrly54uvrq6amZm5uLnaRUgRkx4699+/3yMq68/HjgZCQBceOQWz7pQQgJAQREfj3X6p1SJFgzp+P9fb+e/PmnUwm86dT6elISMDy5Y/S022rqmaWl68oKSlPTi7w94eSEiVia8fdHYaG2L6dah2ip6Sk5ODB40+fvkpOTqZaCxe4j5Hmz5+fk5MDwM7ObsqUKfPnzyeEqKio7N69W1lZWbwKpXCnsrIyJGTLvXvPfH3d3dwGcA6yWHj9Gg8efP13924Bg9EcAGCor18k4XnQDAwwfz5mzMCFC1RLkSKRXL0aP3r0pvz8JRcunLp3b1X//stfv8br1/j3X7x5AyUlNG8OIyNDOv0ZUAmUKStn6es3pFp1LWzaBEdHDBsGvd86BKmrq/eNG9pMpn6/fpP/+eeoiYkJ73VjYy+9f5+6Y8f+TZtaKolmUEPjZTtETk7O+/fvmzVrpqamJgoRAhMVFRUbG3vw4EGqhVCAt/fsw4e1KiqcVVUXTpgwv6ys64MHePoUBgawtoaNDWxsoKX1fsCAwSUlAxUUrq9fP2nUqGFiEFZcXNywoYBPHwYD1tZYvRqursIVJXFUVFTIyMjICbSPrC5/4XrN1KmLtm7tCvQGKhUUeri63mrWDM2bo1kzNGsGdfWvxbZt2xsYuFVOTm7r1oB+/cSR8Ki0tFRJSfBUKv7+yMrC3r3CFSVZGBi0y8xcAWyl0XqZmOg0buypogIVFairo0EDcD5raHz90KAB1NW/fv7333+GDl2Ql5chL9/Xza3s6NEdfPXLZrNZLFatPzTub4SfP382MjL69l9tbW3xby+TUjMJCYkVFXcAelHR5JiY676+XT08YG39U4iWJs+fx9++fdvCYrSZmRlVUnlHTg6bNsHXF717S+50lhSqkJOzotGOEtJOVva4u7tVdQPgyZPHTZ5c12y64mTJErRogTt30El82/rFjb6+xpcvjwmpVFePCQxcr6uL0lKUlqKwEMXFKC1FXh5SUlBS8vV4fv7XDzk5t8rLfYF1VVWed+5MFJE87obQ1ta2UaNGfn5+I0aM+DPHnhJOfj6qqqxptF2E9FdTi1q7dqxzNdsfNDQ0+vfvL151dcLJCe3bIzgYy5dTLUWKxMBmY9kynD7tMXly2rVrQ9q1s9y06fdxrGrQACEhmDoV//wjQU6twuXs2d1Dhox7+fLx1q3bhw1rw3vFGzfs3NzW5edX0ekXWre2EJU+wo3w8HA7OzsAKioqo0ePjo+PZ7FYXEtSS2Rk5MiRI6lWIW6SkoiZGZk4scjXd56NTb/t2/dQregHioqK6thCairR1iYpKUKRI6GUl5dXVVUJVrfuf+H6RXExGTSIODiQzExCJPLyS0pK6v6E7N6dbN8uFDkSyvnz5/v06SNAxT17opSV1QYO9MzPz+e3LovF4uWHVm1kmaSkpJcvX86dO/f69etOTk6NGzeeP3++dBMhtRCCsDA4OyMoCNu3N4yICL527ejEifVpFogXGjWCi0t869btDQysly9fR7UcKVTy7h06dYKODuLjf3N3kvnz30+d2kVb265jx/7FxcVUy5Egxo3zbNasydKls9S/rQMLm5pWdy0sLJYvX/7u3bsLFy507tw5LCysWbNmItIhpVby8uDqiqNHcf++qFLmSg4XL84pK4vNzLy/ZUvC8+fPqZYjhRri4tC5M6ZORXg45OWpViNiNm5cxmIF5ebef/DALSRkK9Vy/ixqd3OSkZH566+/jIyMVFVViTTiNkXcuQMbGzRvjuvXIbY0SVTBYDBYLAVAC5BhMlunp6dTrUgKBWzYgDFjEB2NCROoliIWsrPzgCYAGIwmGRm5VMv5s6gp1kJOTs6hQ4f279//4MEDVVVVDw+PsWPHikuYlK8Qgk2bf9ue6AAAIABJREFUsHo1tm+HuzvVasSCnJxcp04tEhJmlJQY0mjx9vZLqFYkRaxUVmLSJDx4gNu3wc9+s/rN/PnjJ04cVlzcj8U6Pn36Aarl/FlwN4Tnz5/fvXv3hQsXmExm9+7dIyMjBw8eLN1KLzbKy8t37tyXnp4zbJjn0qWmWVm4dw9NmlAtS4ycPLn7woULnz4VLlt2JTVVxUJkzmJSJAQGgxEYGJaQcL9r166XLk00Nqbdvo0/6pHj7u5qadny2bNn69fH3LxpILERoH5LuBtCX19fBQWFWbNm+fn5mYomKmVmZmZRUZG5ubk0l8WvDBgw5vZtu8rKFuvWeXh7x546pfunJfCk0+kDBgwAUFWFiRORkADpbfJ7s2LFuvXrC8vLV9+4Edy794EjR8b8gd+4ubm5ubl5q1bo3BmDB8PAgGpBfwzc1whjYmI+fPgQFBQkCivIZrN9fX3btm3r6upqbW2dmZn5UwFCSL9+/XR1dWk0WmJiotAFSDiEkOfPP1RUzCNkiJzc4H79Ev80K/g9U6eisBBHjlCtQ4qIuXLlTnn5NMCMzZ6opHT7D7SC32jWDN7emDePah1/EtwNoa2trehe1C5evHj16tXXr1+/fPnS0tIyICDgpwI0Gm306NGJiYkaGhoi0iDJ0Gg0JSU54DlQoqJyrXnz5lQrohJZWezYgblzUVhItRQposTJyYFO3wi8UlXd0r9/Z6rlUMySJbh5U5qhSXxU6ywTExNz+vTpz58/M37MERIXF1fHLo8cOTJ8+HDOjpCJEycOGDBg27ZtP5UZMWIEgD9z1pTNhr7+LiZznqJi3rx5E1u2bEm1Iorp0AEuLli8GJs3Uy1FisgoK5vdosUWQ8NVrq7dvL29qJZDMSoq2LABU6bg0SP8yRNCYoO7IZw9e/aGDRuMjIyaNm0qWFzgGvjw4YOjoyPns5mZWX5+flFRkeqPITJ5hMlk5uXlfZ/Xo3Xr1goKCsIRShEbNkBGpsWHD+d+12BLAhAYiJYt4eWFDh2oliJFBNy4gZMnZR8+nKmlRbUUiWHQIOzZgw0bpHOk4oCLIWSxWDt27Jg8efKmTZtkBHoYP3v2bNOmTb8eDwwM1NLSKikpUVRU5Bzh5NQoLi4WzBB++vTp3r1748eP5/xXVlZ27dq1tra2AjQlIbx8SQ8MVIqPLy8vZ/NSvrS0VNLem0UhSU4OK1bITpokn5BQ9nuMD+qSfUICv/S6UFhIGzVKeePGCgUFVklJ7eUl8PLLyspYLJbA2SeqIziY5uioPGBAubExT08DCaeiooLJZJbw8h3/ApvNLisrE6Aum83m5dWIiyHMyckpLy/39vYWzAoCUFdX79ix46/HOYL09PTy8/M5R/Ly8uh0uq6urmAdmZqaFhbqy8kZ3LsXI1gLEkVlJXx9sWEDLC15dRsnhDRo0ECkqvhFRJLGj8fRo4iKajBlitDbFje9eg2Pj08EyKRJXlu2BPFb/cuXL/r6+qIQRgl+fhg0CIMH85ptRALveRqNVpc0TNXRsiWmT8eSJconTgi3YWqQk5MT+Luj0+nKysoC1OWkYaq93K/hR5lMpp6e3pkzZ/gNb8ojCxcuHDFiBOfz4cOHLS0tqyupqal569atGpqKjIyk0Rh0eo9jx44JWSUVTJ9Ohg3jr4oEBiAWnaQXL4iODsnIEFHzYiIxMZFOtwWYQCmNZrpvX9m1a+T5868RpWsmJiZGVtaITrdUUPgr5beISn74MGnZkpSV8VFFAu95oQTd5kpFBTE3JzExomhbrPj7L6fRtGi0Bnp6lgwGg9/qVlZW9+/fF6BfHoNuc88+cejQIUtLy/fv3wvQca28f/9eVVU1IiLi6tWrpqamu3bt4hx3cXGJiorifI6Ojg4PD1dRUZk3b154eHhhYSHXpiIjIwECzHVwOHjzJpHIDBm8cvEiMTYmeXn81ZLAh4JIJS1YQOp1upGnT4mr60XAGyAAAbooKOTIyRE6nfNfQqMROp3IyRFlZaKmRrS1SaNGpFkzYmlJOnYkcnJOwH2AAPs7dOhH9dXUldRUoqtLkpP5qyWB97zoDCEh5NIlYmbG31hBApGR+Qs4BjjTaL6rV6/mt7qoDSF3Z5lTp05lZmY2b968RYsWOjo635+qu9eoiYnJhQsXNmzYUFxc7O/v7+Pjwznerl27b9mAnz9/npGR4enpWVBQkJyc7ObmVt0iIo22Ajj+4cOS8eNRWIjBg+HuDkfHepbWKycHPj6IjMQfuWGEDxYvRuvWuHoVPXpQLYUfXr1CdDSio1FQABqtG402lZD5NFqepmZuTs4P/iEfPyIvDwUF+PwZ+fkoLEReHvLyviYpZbEqAc7vUTc5ufmoUejaFV27wtz85x7Ly8uVJDu1MZuNsWMxcyZsbKiWItn07g1bWwQFYcUKqqXwT2kp4uIQEwMWSwvguIYY5uXlUSzrF2iEWxztoUOHflvG+4m6G0IhEhUVFRoaGhd35dQp7UWLMHkyNDRw/DhevYKzMzw8oKf3aNiwCaWlDFvbFmfP7hO6B6ywGDoUTZogOJjvisXFxZKWOVnUki5exPTpePIE//e4kiDYbPbs2cvPnLnUsmXz/fvXl5Zqnz6NY8fw8SMGD4a2NrZtw8yZ8PMrWr06QE1NbcGCBbKyNcX7/YmVK4OWL99LSF8a7aSq6r5OnXro6+PaNZSVwdERDg7o3BmmpgVOTkPS0yuUlasuXYqS2Iwx69fj5Elcv873mFUC7/nS0lJRrBF+IyMDbdvi5k1I5qbiwsLCI0eOqao2GDLEnfOM/fABly/j3DkkJMDCAkVF+PzZs7z8ISEl8vIkNTWZX78Qa2vrXbt2CeAIyVkjrP3JL8DLpuTwfWLed+9I586kd2+SlkZSUsjataRjRyIj0wN4BxAlpaX79kVSq7Y6du4k1takslKQuhI4TSQGSYMGkYAAUXciCHv3RjZoMB1g0Wgn1NS8DQzItGnk5k2Snk5cXYmlJXn48GtJgRPzPnjwYOXKlSkpKbm5xM+PNGlCLlwg796RvXvJmDHExISoqKym0fYABLjfo8dwYV6e8Hj2jOjoCJh7WQLveZFOjXJYt44IlNRW5JSXl5uatpeT26isvNTS0vXvv0mLFkRfn3h7k+hoEhxMdHRIUBBhMsny5cstLCyKi4sF6IWaNcL6wk8Z6hkMEhREdHXJgQNfjzRq1AGoBAiNtn/Vqg3UqKyRN2+Ijg55/lzA6hL4UBCDpE+fiLY2eftW1P3wzfTpi4FYgAAVRkb2nGdjdDTR0yP+/j+MdYSVof7aNWJhQQYMIKmpX494ec3/v4a01q2dBb0UEVJZSaysyJ49AlaXwHteDIaQwSBt2hAJdApMTEzU1JzGWeGWk+s+f35hUhJhs8njx6R9e+LoSF6//lpS4Az1RPSGsNp3+bt37w4fPrxNmzatWrXiHAkLC9uzZw+/b6biRFYW/v6Ii0NoKIYORW4uZs8eq6XlqqKyUkFhQ3T0kHfvqJb4IwwGvLywdCn++Ogx/PHXX/D3x9SpVOvggjOdvgY437DhtLFjXYqKMHo0li3D+fMIChJJatmuXfHwIWxtYWuLsDCw2Vi8eKy+/gI1tUUNGgxJTZ166JDwO60jS5eiSROMG0e1jnqFrCy2bcPs2RBoG54IUVc3Kil5AlQCuZqauatXN2jdGitWoFcv+Pjg2jVI6tz8D3A3hOfOnevSpcuLFy9MTU0L/x/kUUFBYfny5UTic/NaWuLePZiawtISJibjRo2ysrW9d/RowJQpjTp2lKzwzQEB0NDAb7AxTvzMnImMDBw/TrWO/0MI/P1x9WqnhQtHWFiEDB5M79hxTps2UFLC/fuwsxNh14qKWL4cN2/izBk4OoLJbH727M4xY0oOH150+3a/wECMHo3SUhEK4Itbt3DgALZvp1pHPaRzZ/ToIVkuM2lp8PJqbGc3Sl+/o4mJ8/7962/fpltbIzkZDx7Az6/+JI3h+p5oamrq5eXFYrESEhKMjIw4B1+/fg0gLS1NgPdTEfHT1OhP3LpFGjacJiOzArilpdX1/v37ycmkaVMyahQpLRWnzGrl6evXdVecBE4TiU3SuXNf5OVd9PVt+/XzLKPUu7yiggwbRpycyNmzNzQ0+gCJsrLzVFWXJCRUW0VYU6Pfw2aT/fuJltY/SkrtabRwTc1+u3ZFlpWR6dOJhQV5/Fiw3oRJQQExMSHnz9epEQm858UwNcohJ4c0aLDF2Nje2XlUenq6GHqsgTt3iKEhCQxku7iM0dXtr63duV27YCMjcuIE9/L1bGo0KysrJSVl1qxZdDr9+1BGnL0Nv2ZNklgcHKCmdofFWgo45OZOjomJt7FBcjIYDLRvj+fPqVFFCLlz505Cwt1x40h4OH6jCCHiJjJyGYMxPjPzfnx8u5CQLVTJyMtDz55QUEBsLOLiLuXnzwLsmcyV2tpXu3UTqxIaDaNHo0+f6PLyIEL88vIiN2+OVFJCWBgWLECPHggLE6ue7/n8+fPdu3enTq1ydkb//pTJqO88fhzPYt349OlKbKzX0KGTKVQSHQ1XV+zYgX79nty5w8jKOp+Tc+PZs2MPHlQMHkyhLgHhYgg5xo/8MgWanp4OoH7lqW/VqhmdfgzIVVI6bm9vDUBVFYcPY/JkdO+Ow4cpkNS793AXl139+kWUl48YOJACAb8N79+nEWILoLKy3Zs3qZRoePsWHTuie3fs2wd5eTg4WMnLHwXyZWUj27dvS4mkNm2MFBSSAdBo901MGnEOjh6NW7ewbx+GD6cgodWuXVFWVsN69z5w9KjjokUF4u7+N+Lhw2cVFf0BJUJ6f/iQRokGQrB0KRYsQHw8XFw4c4pf35caNIBAQaOph4sh1NHRadKkSWRkJH5MhLR161YdHR2J3ZbElYMHNw0dmmBhMUJGplerVj2/HZ88GZcvY9ky+PmhvFx8ej59+vTkSXlu7u7Kyt2VleWfPn0SX9+/HZMnj9TU9KHRdsvIzBk7drj4Bdy5A0dH+PsjIODrWkirVkPk5S1btBjq5fUsPJzvIKJCYebMiT16PNbXt5OT2+Tru+rb8ebNcecOtLVha4ukJAAoKytjs8URzXnVqs05OXHFxdvYbM9z5yRmXbce0rdvDy2t7cBlGm2prW0n8QsoL8fw4YiPx9274LhRtm3bVkmJpqTkqqPT3cdnsKIEbu/lAe6beVesWDF69OicnBxzc3MGg3HixImjR48eO3YsLCxM4EjclKClpXX48DYA8+dj4ULs3fvfKSsrJCdjwgTY2eUpKnpnZKRZWDQ5fXq3YHkweERZWZnJzOO4GhOSU79eryWNsWNHtGxplpSUHB+/5/jxFr16ibX3kycxaRIOHECfPl+PsNmYMAEbN8708ZkpVik/oqioGBMTCSAqCsuXw9kZ3/Z5KypiyxacPIkBA1gaGl55eR/p9JK9e9f16yfav52srCxQBijJyxcpKUmDJwlOq1atLlzYtGfPybw80/fvlzCZ4CceQ13JyICbG5o3x9Wr+JbR4cYNmoxM5D//pGhrK9fjWPDVLR5GRER8H1ytQYMGQUFBbDZbgOVK0VGzs8z3lJaSxo3JjRtcTnXuPAc4DBBZ2d0zZiwRssQfYbFIkybrVFTaamtbrVixvu4NSqDjgPglFRURU9O6umDwCMcrZ+NG0qjRf7vjOWzdSrp0ITz+REThLPMTbDbp2pXs3MnlVETEaRmZvwEC5JmYdBBMBu/4+sbJybXV1e3doUO/ioqKOrYmgfe82JxlvsFmk379yLJl4uirsLCQzWY/fEiMjcmqVT/c4RUVxMKC8JigQZKdZaodTvj6+o4ePfrBgwcZGRmampq2traSlvqEL5SVERiIGTOQlPRzSCd5+c/ABABMpt3bt7dEKmPdOhgbz375chqdDomN91bvaNjw6+rX48fQ1hZVL/fvJ7u5ja+qUpKT09LQiE5MVDQ2/u9sZiZWrEBCggT5i9No2LIFPXti0KCf/yyKiiWysjosFoCG5eVMkcpITsbZsz1fvUpq2LDgp8DFUgSGRsOePbC2Rt++4JbyTjgUFhZ27uyanS3DZOYRciA8vM2QIT8UCAhA27b4DXwdagqOJy8v37Fjx0GDBnXt2rVeW0EOI0ZAXR0RET8fnzp1pKamH40WISc3pajIU3RLhg8fIjQUBw5AQUFOagWFS5cu8PLChAki7MLXd8HnzyezsxMzM7uMGxf5vRUEMH06Jk6UuMAIrVvDwwPLlv18fOBAl0aNTmhozFJRca6oGPPokagElJRg5Ehs3gxTUzmpFRQu+vrYtk20m0TDwna+ejXqy5erubkH//pr6U9W8Nkz7NyJ9etF1bs44f5GeOPGjaqqql+Pq6qqmpiYCJxHl3I2b4aTE4YM+WGAPGiQi6mp8e3bdzt02L5xY2snJ5w5A6FfYlkZPD0RFoafHqBShEVAADp0wP79GDNGJO2XlpZxMj8Qol9RkfX9qdhYPHqEAwdE0m8dWb0aLVpg7Fi0a/ffQVVV1WfPrt29e9fQcNLTp8369MHBg+jZs/pWBGXGDDg4YOhQ4bcsBcCgQTh3DrNnIzxcJO0XFpayWJzMJlrAD/aWsyIeGAhDQ5F0LW64Tpjq6enVUMXBweGtZMR55H2N8BszZpAJE6o9y2aToCBiaip48M/qmDSJjBol5DaJRK6XUCjp+XOip0c+fBBJ456eUTIyXdXUFhgZWX3+/Pnb8dJSYmpKrlzhrzUxrBF+Y9cuYm9f0+LlnTtEX59ERAgmp1qOHydmZkTot4ME3vPiXyP8rmtibk7OnhVJ4xcuvKfTrVRVZ+vodDhz5ofswJs3E0dHXlfEOUjyGiF3Q3jy5EktLa3JkyfHxsYmJSWdPXt25MiRRkZGZ86c2bZtm6GhYYsWLZhMpgCyhIsAhrCwkBgZkXv3aipz9CjR1SUXLtRJ2/fExhJTU1JNduE6IYEPBWolhYSQzp2J0O9NThig+Pi3ly5d+ilN9Jw5ZMwYvhsUpyFksUjHjmTfvprKvHhBTEyE6XyRmkr09Mjdu0Jr8BsSeM9TaAgJIYmJQohR9Su5ucTMjOzdW3jt2rWfotikpxNdXfLiBX8N1j9D2KlTp5CQkJ8OTpo0iWN1kpKSANy8eVMAWcJFAENICNm7l9jZ1ZLOPjGRGBiQ7dsF1/aNrCxiaEiuXRNCU78igQ8FaiWxWKRbNxIaKsw2P30iBgbcB0aPHxM9PZKVxXeb4jSEhJD794mBAcnPr6lMRgaxsSE+PoTBEEzXf7BYpHt3Ehxc13a4IoH3PLWGkBCycCHp25e/97OaYTJJnz5kwQLuZ93cyIoVfLcpyYaQi7NMXl7enTt3Bv7iCTRw4MCYmBgAdnZ2BgYG79+/F94ErVgZMwZKSti/v6Yy9va4eRMbN2LGDNRlzzEh8PaGry+6dhW8ESm8Q6dj714EB+PJE+E0WFEBd3fMnYt+/X4+xWZj4kQEB0PyvUBsbeHigoCAmsro6+P6daSnY8gQlJXVqbtVq8BmY86cOjUihXdWrEBeHnbtElqDf/8NNhsrV3I5dfIkXr6Ev7/Q+pIEuBhCQgiAt2/f/nScsy7I+SwvL19PIwgAoNEQFoZFi1BQY7AnMzPcvo3HjzFsmODRZ7ZuRWYmFi8WsLoUATAxQXAwxowBN38vvpk8Gc2aYfZsLqe2bIG8PEaPFkIvYiAwEIcO1TI+aNAAZ89CRwdOTsjOFrCjpCRs3YrISL5Tz0sRGFlZ7N+PhQvx5o0QWjt0CKdP4/BhLt9gURFmzsSuXf9tqP894GIItbS0OnToMGXKlBs3bnCOEELOnj27ZMkSZ2dnANnZ2WlpaU2aNBGrUqFibQ1XVyxfXksxTU1cugRFRTg54datF0FBGy5evMh7Ly9fIiAABw9CuldCzIwbh6ZNuWwb4Jd16/DwIXbu5HIqIwOrV2P7dgnaOFgzmppYtgxTpqDmRGqysti5E337olMnvHlD/vnnnyf8vFyXlMDTEzt34q+/6ipYCl9YWGDpUnh6gsGoUzuPH2PWLJw9Cy0tLmfnz8eAAejcuU5dSCJcJ0xfv35tamoKoGHDhqampkpKSgBsbGy+fPlCCElMTJw0aVI9dZb5Rm4u0dcnjx7VXpLNJhMnPpaRaQdEqap6LV368+opVyoq6pSGm0ckcL1EQiRlZ9d1afbKFaKnR1JSuJ8VbJnkG2JeI+TAYpF27cjhwzwVjohgycsPVFcfp609ZOTIKTx24elJJk0STB2vSMgN9j2UrxFyYLOJszMJCBC8hdxcYmpKjh7lfvbuXWJkVMtKcw1I8hoh932EzZo1e/r06fHjx588eZKZmfnXX3/Z2tq6ubnJysoCsLe3t7e3F6e1FgWcAfLUqbhxo5ZBPY0GdfWzLNZCwK2oaOiePY4rVvxda/uLF8PMTJqGmzK0tbFjB3x88OgRBIgG8f49vLxw9Ci4TnxcuIAXL6jJXlIX6HSEhWHYMAwYUPvfpH37Z4qKagUFewBcutS1sLBQTU2t5ipRUXj4EPfvC0uvFP6g0RARARsb9O37w7ZRHmEy4eGBYcO47/usqoKPD8LCoK5ed6USR7Uh1pSVlUfXl9UPQfHzw969OHIEI0bUUrJFiyYqKrdKS12B29nZxra28PTE8OHVbia9ehVHj+LhQ6FLlsIHLi44exYzZ7IXLHivo6PDezj10lIMGoRFi7i7OJWWYupU7NqF+rhK3qkTnJywahWCasuNoaysrKCQBwBg5eeX9O4t7+EBd3fuIwMAKSmYPRtxcVBSEq5kKXxgaIgtWzBqFE6f/qigwOZrAWvePCgoYNUq7meDg9G4MdzdhaNT0qgpxNpvD52OLVswdy6Kimop6eU1YtgwYmTUzt5+/Zs368LC8PEjrKzQuTN27vyhek5OTmZmhY8P9u7lPskuRZysWVMWFdXT1naBuXmPY8dO81KFEPj4oG1bTJ3KvcCiRXBygpOTMHWKk+Bg7N2LV69qKda0adMhQyx1dDro6NgFBvquW6eUmgoHB7RqheXL8fLlfyWTk5OvXEnw9GQuWoS21CRhlPIfQ4aAyZxtYzOxQ4epI0dO4bHWwYM4fRqRkf/lKvmeN2+weTN27BCmTsni2yTpkSNHDAwMNm/eTAixtLQ0qAYBZmlFR13WCL8xdiz5+29WRkYGv7P8ZWUkOpoMHEjU1Mjw4eTsWVafPiN1dXsoKFj17Xuojqp4RALXSyRK0oEDkfLygQABihs35inNQmAg6dCB/Joj4cWLF+bmDhoaNgoKo9LTBVze+wYla4Tf2LCB9OzJU8nS0tLKyspv/2UySUICmTqVGBqSNm3I8uXExWWaltZwRcXpqqo9Kivr+mfhBYm6wThIyBohhy9fvmhrO3FyvWlpOX/8+LHWKsnJRFeXPHvG5dSqVeutrPoYGs4JDS2uo7D6sUbYpEkTDw8PCwsLAAMHDiyq9S3pd2HixHf29sP27NFv2PDLjRsn/+LZ3U1JCR4e8PBAXh6io7Fw4bXnz9UJOQhUPHpkD9Q23ypF9LDZ5Fty6U+fiJMTevdG796wsuI+8o2NxZYtuHePi3f4mDFz//13J9BSXn55bOwhb2/RhDQVC1OnIiBgnobGFW1tpcOHN9nZ2VZX8qeUmTIy6NYN3bohLAx37uDo0YoLF5JYrDsAlJSm3L+f9Bt4D9R36HQ6jfY1o0h+PqNnT7qLC/r2RZcu3Cfzs7Ph7o5t274m2v2egwePBgc/Ly4+SKNFP3myGNgoYu2U8Z8hbN++ffv27TmfV3LdSPmbEhISzGZvyM3tkpd3afHidfv38/1la2pi4kQ0blw5dGiDkhIA8pyQPbT64ln/+zJkyODQ0P5ZWU8I+TckxF9PD5cuwcsLubno2RN9+qBXLxgY4N69f6ZNCygtZWZkLDh/vquREZemcnPzATMADIZFauoncV+JUImPv1xVlV9a+qCg4KOnp9fr1zf5bYFOh4MDOnaUO3KkLDu7ElCQkckQaVJrKTyira09dGjn6OhuAH3gQNuJExtduoSAADx5gs6d0a8f+vSBuTny8/NnzVrx9Om/paUenp5juS7+3bz5sLh4BKBFyJh79/qK+0rEiBjTG0sqFRVVQAMAhDQsK6sQuB0nJyczs9DPn8fTaO8nTfKSWkFJQEVF5fHj+NevXxsYGKirqwNfA8SkpuLyZcTEYPZsGBoyUlL8SktPAnKqqoNat44HuDzQGzUa8+mTu4xMNw2Ng6NHnxTzhQiXjIzMqqo2AADj4mLBE4/JyMisWTNv4cL2gNyQIb1bt24tLIVS6sKWLauXLs1is9mclPF2dl/jh1y5gosXERwMBQUwmTPS0nqzWP6yslO7dzcGuCx6W1v3oNHWE6KionLcxUUE2UkkhmoN4ZkzZ0JDQ58/f66srJyWlgYgJCREXl5+5syZYpQnDlasmJac7MNgOBQX39LSihK4HQUFheTkyw8ePNDV1W3cuLEQFUqpC3Q6vUWLFj8d/Osv+PjAxwcsFi5cyBw50hQwBaCo2PbDhw+WlpY/lV++HAUFE+Li2mVmvune/WLN6Vkkn759++jo9M3KYtHpj5SV+zKZkBV0SOzr6zlu3HAWiyUvLy9UjVLqxK/J8tTVMWQIODkFnz6Fo+NLFusAACZz+K1b93v0+NkQvn2LVat6zZpVmZ4eZW9vOWmSt1iEUwN3r9H9+/e7ubkpKiq6urp+O6ivrx8YGMhiscSlTUzY2dm+fp1w/vzIN29u3LzZKiRE8KZYLFZRUdGfs7z6GyAjgwEDGunrZ8jIHKbRTigpPWnevPlPZbZuxcGDuHQJ3brZDB8+vL5bQQB6enqPH8ft3q11/vxYC4tV3t51Cqibmpr677//Ck+dFJHTpg2cnTsoKAQB/2hq7ujb9+d9Qqmp6N0bS5di3boBhw9vmTbNT1bgsVJ9gMu1EUIWLlw4Y8aMjRs3Xr9+PS4RBabPAAAgAElEQVQujnPcwcEhKyvr8+fPxr9dblk1NbVOnToBiItDly5QUxMk13lZWZmtbe/MzA6ysu9GjbJav3650HVKEQU0Gi0x8cy6deEMBnPOnNMKP/rJREUhOBg3bkBfnyqBIkFbW5uzUbhzZ/Tvj/HjsWuXIOHiZs9eERWVCGi2bFkVH3+cztUHSYrksXt3qIXF1idP9vr6zu/QocP3p7Ky0KsXpkzB+PFUqRM3XAzhly9f0tPTx/0SE4Uz3ZyVlfX7GcJvGBri8mV06wY1NQwfzl/dhISEz5+7FRevAsihQ3br1i2TLhPWF3R1dYODl/x6/OxZzJuH+HiYmIhdk7hQUsLZs+jdG3PmYP16/upWVVVFRZ3Lzk4CaM+ejU9KSvrpkSpFYlFUVFyyhEt+kJwcdO+OsWP/rOQhXIZvnLn+8l8SLnz48AFArWGW6jtmZoiNxaxZOH+ev4pfvjQsK8sAAJQWFZG8PKkVrN8kJGD8eJw7BwsLqqWImAYNcOECrl/nO01KaSm9pIQFsAEUFZXn5kqjy9dvCgrQpw8GD8b8+VRLES9cDKGmpmbLli23bdv2/QYAQkhwcHCjRo2aNm0qXoUU0Lo1YmPh54dr13gqz2QiOBgLFnSxsVHQ0Wmvq+vQt29AmzY4cEC0OqWIjqQkDB+O6GjYVrvF7rdCXR2XLuHMGaxZw2uVS5dgbS3btKmPlpa9jk4PCwvlsWNtwsLqtNwohULKyjBwIBwcuKch/L3hvv4ZFBTk5ub2+fPnli1blpeXb968+dixYzdv3oyMjPxDpvusrHD8ONzdcfZsLeFrnz3DuHHQ1kZyMq1Ro22VlZXy8vI0Gu3RI/j44OhR7NghTUlTz3jzBq6u2L79z0qnrK2Nq1fRtSvk5PB3jVHlCwrg74+4OEREoFevqYWFoyorK3V1dV+8wLhxOHECu3fD3FxcuqUIg6oqDB4MMzOEhVEthQq4r2y7uLicOXMmPT19y5YteXl506dPf//+fWRkpJeXl5j1UYi9Pfbvx4ABePSIewHOi2CPHhg/HrGxaNQIABQUFDhjBSsr3L0LR0fY2UE6TK5HcPzlVq/G4MFUSxE7urqIi8OOHTVFlTx/HpzdJY8fo1cvAFBTU+M467dsiTt34OUFe3sEB0vv+XoDgwF3dzRsKKDD1G9AtS5eAwYMePnyZWpq6v3799+8efPp06c/ygpy6N0b27ejf3+8fv3zqefP0akTrl1DcjL8/LhXl5ODvz9u3cKpU+jatfYwx1IoJzsbvXphxow/N39Wo0ZISEBQEHbv/vlUVhZGj8bMmThwAOHhaNiQS3U6HX5+uHsXFy/C0ZHLD0eKpMFiYfRoyMhwT0n/h1DL1pBGjRo14rzpCJW3b9+uW7cuNze3T58+3t7eP023FhcXR0dHJyYmVlZWtm/f3s/PT4m6zC6DB6OoCH36ICGBRchHfX19eXnldeuwbh1WrcL48bUPoMzNkZCAiAg4OmLyZMyeXX7mzAkajTZkiLtifUzk8zvCCbFWWcksK1s4ZozjrFlUC6IUY2NcvgwnJygrEzOzJEVFRUtLy2PHMH06hg7Fkyf4Mf4oF8zMEB+PiAh06YI5czB3LoqLCzIzM5s1aybdXyEhpKamjhgx7dOnNHV1Fz29pefO0X7rjYK1QMGlFxcXd+nSxdvbu2/fvv7+/sXFxT9Fq0lMTDxz5szAgQPV1NTWrl177dq1U6dOiV/nN8aOxadP+RYW/dXUGrHZb7W01puZdXvwALyPEGg0+Pmhd2/4+bFDQpzZ7F50OtaudX706OofsuYqyTAYDDc3v8zMk4CcsvKg6dO5h1j7o2jWDOfPszt0GKSoqCUrW6ykpKetveX8eT5chzj3vJMTfHywZ8+pnJw1srLmOjqf796NaSBAomQpwmbkyOmJiXMB+/T0SQsXnlFUdKNaEZVQYAgPHjxoZma2evVqAIqKihMmTJg+ffr348Q+ffr07fs1wGurVq3atGlTVlamXOsoVJTIy+9jMLyzs32BTEVFzwsXugnQiIkJtm9/b2OjU1S0EEBa2uOUlBQzMzMha5XCJ5mZmVVVX0OsqahwD7H2B0KnP1NW/pqhXkXF8fHjIi0tvscHTZvi2jXo6ITk5cUDDQsK1h09eszH50+dd5YkUlI+A/YAncVykgYGomCaIikpqUuXLpzPXbp0+fjxY2Zm5vcFvn9JSk1NVVdXp3BqlAObTWg0zvS5jJqa4D4AOjo6CgopQBlQWlj4bsgQnR07ak8LLEV0lJUhLKxRUVEGnX6YRjuhrMwlxNqfibKyspzc1wz1SkqlysoC7hGk0aCqCoAAYDDYO3fS4uNBiNB0SuEXBgNr1iA/v6+CwlTgmLb2Onf3/lSLohiRvBEyGIyMjIxfj+vr68vLy2dmZn4LgqysrKysrJyZmWloaPhr+aKiounTpwcEBFQ3f/jx48erV686fZcsfOXKlW1FkCTb09N9164hxcUJNNrzVauWlZSUCNYOnU4PDJy1bFkXGo22bNksPT25w4eZCxbIdO/OGjeO0a0bi9+J0tLSUkmbXJVASdVx547MlCkKrVuz7949fPDgbgaDNW3aQQaDwWAwRN11RUWFjIyMnJwg1kU8f2F9fX03txYnT7an0apmzhzHYrEEvu1Xrpw1a1Z3wFRbO2vo0NOzZ7OLizF2LNPLi6Gjw7dJlMAbrKysjMVi1Yvlz8eP6dOmKerqkqSkeQ8enHn58tXgwduMjY0F/nJ5p6KigslkCtYRm80uKysToC6bzVb4NbnorwiQ87dWnj592oQbDx48IIS4u7uvXLnyW/pgGRmZV69e/dpIaWlply5dJk6cyGazq+to165dTk5Ocd9RXFzXNMrVUVVV9fTp08LCQqG3nJdHwsNJmzbEwoIEBZHsbJKbmztz5hIPj4lJSUk115XAbN0SKOlXCgvJ9OnEyIicPEmNAGoz1PPOTxnqBaawsPDt27ff0rjfv0/8/IimJhkwgERHEyaTEELevXt36tSptLS0mpuSwBtMojLUV0dpKfH3J4aGZP9+agTUjwz1QqR169YpKSnVnf3rr78+fvzI+cy56X99HSwvLx84cKCZmdnWrVtrGP0pKCjo6+v37CmORFlycnIiSremoQE/P/j5ITkZO3eiWTPQaKMKC4ezWH2vX5+SnHyiOsddFouVl5fXkKsbu5TquXABkyfD0RFPn0JDg2o1ko2w1uZVVVW/T9tra4vwcISG4vBhBAVh9mx06nT56tXllZXOysorY2MjbG1tuLZTUFBw/fp1KysraaYzvvh2zz9+DG1tajR8+vQpNze3srKSp1e0H2GxWKJNfCSAja0jiYmJenp6OTk5hJAVK1Y4Oztzjl++fJnzylhZWdm/f38PDw8Gg1FzU5GRkSNHjhS1YDGTm8tu2LAdQABCp692cDjp70+2byexseTlS1Je/rXY7dt3DAysdHR6WVp2F917sABI4ID9G9nZZORI0rQpuXaNYiX15Y1QPCQlEX39IcBbgAC32rSZGh5OTp8miYnkzRvy7XJTUlIMDKxUVedraztER5+iVPIPSOAbIYvFYjKZhJCMDDJ0KDE3J/HxVOpZtChIVbWdrGwTc/OOfD2vysvL27XrIyurqqXVLCHhOr/9UvlGWDP29vaurq5WVlampqbv3r2LjY3lHF+/fn27du2sra1PnToVExPTuHHjb24L8fHxf84AUFOTZmLS8NmzK4SYqqrGursPKy/Hw4c4dQofPuDjR2hqwsQEL18uLSg4C/xVULB15859s2dPpVq4hLJ5c8TWrZFNmzbp3z8wIMDQywtPnoBq7yspP2BnB3t7jVOnPhBiRqe/V1LS/OcfZGcjOxtfviArC0wmtLXBYER++bIEGAzkBgQM8/D4oz3+ayAkZFtoaDhAc3AYd/v2DF9f7N8Pajct79kTXVQUAGx7/95+0KCLpqZD6HT8msFBUfHn32ZS0uGHD7szmTm5uYGTJi15+fK6KORRs4UyPDw8JSUlMzPTxsbm26byw4cPc3wHBg0alJeX93353z7lxU/ExOybOXNlRkb24sWLnZ1/2F9BCDIy8P49PDwqCwoaAmAy1XJyuLgmSQFw8+bNZcti8/NjXr++k5Aw6dq1MzVHjpVCFWFhS1+98szOnm9srHnp0lF19R/OlpcjOxvBwQ137vzCZAL4wmJJNyNyp7CwcO3a/Tk5DwDa+fOOV6+OdHTUoVoUZGXpQBkAObnMli3tWrUCm43Cwp+LVVQgP/+HI4WFFSwW5/mvVFlZJSp9/L5pShRCnBoVeJ6KKqKjT+nodNTQmNqwoU3jxhn//EO1oP8jURN3mzdvo9F2ceaZDQ3t6thaWlrajRs3SktL6y5MOjXKlRo84wghxcXFtra9tbUd9PRs9fWfLllCJGQ+UqKmRtPS0nR1+3PueR0d97dv39alteLi4ps3b2ZmZtZR1ZkzF9TVm8jLq7u6juPrb5WTk2NiYicjo6Oubrlv32F+++VxalRqCElaWpqFRWcdnU6tWnX98uWLUISJh/T09MuXL5eVlZ0+TXR1SVAQqfExIiYk6jH98uVLJaWOwBUlpYChQydUV2z//qOGhtYGBlabN++urszBg8e0tTtpak43Nrap+3OBWkPIZrOHD5+krd3W2Nju5s3EOrYmZjg/0uxs0qsX6d6dSMJPVqIMISGkeXMPWVk/DY1Jjo5u1Y0t0tPT+/QZaWracdmykOra+fDhg6GhlabmDB2d9jExF+uo6vz58z179hSgYnl5ubm5eWxsrAB1JXeNUNL4++/A168XE9InN/f04sVrd+5cS7UiXjEwMGjQoIGSkpKrK6ytMXw4bt/G3r3Q1KRamcRQXGyhpLTZ3T3a2tpkypR5XMuUlZXNnRuUnZ0IyCxa1F1RcYCSkm5pKQCUlICzpTA/Hzt3huXnXwIaFhTs37Pn0IIF9TggaWxsbGwsvbDwEZAxduyQt28TqVbEB5zwGtraiI3FypVo1w7R0ejQgWpZEkNGBvLzj+7Zc9PYmHTp0qU6r/uRI6dfvz6ekG7r1vmpqZ1v02YAi4WiIjAYKClBRQXKy3HmzL709KXAICBz4cIxzs596qhNRqCo3oqKiioqKjo6Ipzg/dMNYVUVHj0qJKQRADa70alTBS1awNUVpqZUK+MTY2PcuIHFi2Fjg8OH0akT1YIkACYTEyZgyxa7ESPsaihWUFBApxsBSgAqK82OH8/W1tZVUQEAFRXIywOAujoUFBSAQqAhIfkJCUp+ftDSEsdViIK7d3OLizlrz3qpqZWhoXB3R5MmFKviFxkZLF8OW1u4umLBAsyYQbUgyWDaNEyaRBs1yrHmYikpHwnpBdBKSpxDQ1+0bDmA470iK4uGDaGgAGVlKCgo0en5bDaAgvR0pY8f8bv6LP65hpDNxokTWLgQamp+6urjWCxnRcXzS5du//dfODpCQQEDBsDDAw4OX/NLlJeXP3jwwNTU1MDAgGrt3JGVRVAQ7O3h5obZszFv3h+aWuwba9dCTw8jRtRSzNDQ0MCAnZW1QElJvmnTtJgYC67D1i5dVnp4uLDZ2rq6NHPz082bw9cX8+fjJ7cOCefZMwQE4PZtZ1XV3lVVpYqKj/r1G5SaCgcHaGjAwwPDhuH/cZ8AgBPygjq9tePigps34e6O5GTs2FF7Zozfm9hYPHmCqKjaSzo7O0VEzGGxnLS1N8bG7uAaXregwM/BYWBubrSMTJaHx147OwwejJUroasrdOFUI8Csq+Qg2Bohi0Wio0mzZsTBgSQkEEJIWlrauXPnMjIyvhW4f58sW0ZatiS6umTUKLJ/f3qjRjaamtN0dDoeO3ZaqBdRJ7guF338SOztycCBJDdX/IokZY3wzRuipUXeveOpsI8PY9iwU8eOHauoqKihGJPJzMvL43z++JH4+RFdXbJsGREg3JD41wifPyceHl/XksvLSUFBwYkTJ+7du8c5y2KRmze/Rttp2ZL4+5ObN8mCBYE6Om11dduEhx8QTKoo4Hr5ZWVk7FhiZcXrNy5cJGSNsLSUNGlCrlzhqfDFi0wDgwPTpy9JTk6uuWROTg7n6rKzib8/0dIi/v5EgHtQkiPL/FmGkMUiR46QFi1Ily68bql++ZIEBZFGjUKASIAA+c2bOwooVwRU90xkMMiyZaRxY2bfvtONjGw7dnRJTU2lVpI4YbNJz55kwwaeCqelEU1NkpMjSEevXpERI4ieHlm3jpSV8VFRnIbw9WsyciTR0yOhobWLZLHIrVtk1ixiaJgiI9MLYAOVOjrWNQ8RxEkNlx8eTrS1SVDQHSenYQMHjvv333/FI0lCDOHs2WTcOF4Ld+1KoqIE6eXDB+LnR/T0yMaNpLaQJz8gNYSiolZD+Pnz5549hzVp0mHx4qCTJ0mbNqRjR3L5Mt8dhYZukpXdAhDgY9u2vQRXLGxqfiZOnbqPRvMHCI2W0LPncEmQJB527ybt2n2NYFkrs2aR2bPr1N3Tp2TQIGJkRMLCqgYOHGdgYGtl1evDhw81VBGdIWSz2QEBodbWfSdNmv/iRem4cURHh6xaRfiNPvTgwUN19XEcL3xd3e75+fmCqRU6NV9+TEwunW4HPANumZi0q3k/hrCQBEP44AHR1yfZ2TwVvn2bmJryZ8Z+IimJ9OhBmjcn0dHkyZOnw4dPnjjRPz09vYYqUkMoKmo1hF27utNoV4AqGZmxTZueP39ewI6Ki4stLbtraPSWlbW6e/eegK2IgJofCnPmLAfOAgSoaNrUXhIkiYHMTKKrSx494qlwdjbR1CS1xXnmifv3SatW4TRaACdOWI8ew2ooLDpDuGdPZMOGE4AsGZn1iorzliwhgpkwJpNpY9NLVXUmjebTr98YwaSKgpov//bt2xoa0zj2W0+vT45gb/p8QrkhZDJJu3Zk3z5ey/ftSyIihNBvXBxp2zZfTs4GuE6jnWze3KGGwpJsCOtB3pCauXDhxdKl/214+PwZ8fHYvh0zZ6JfPyQmfiLECZBjsfqOHv2yv6BZtxo0aPD4cfybN4eaNr3PZLYXjnTRM2qUm7b2SuAAjTZ2+HAPquWIiRkz4OMDHpNxbdqEIUNgZCSEfm1t0bv3Z0KsAQDWCQnpdnYYNQpr1uDkSbx+jW/JnUJDtzdp0tHUtOPx42f57eXhw4cHDx78Naj9ly+4eBGBgQgIeFBc7AnosFjeTZokBwQI6M4jIyNz796F48ede/QY26fPXkGaoIKWLVsqKiYCt4DziopFWvXXtZcftmyBsjJGj+apcHIynj/ntXDN9OyJTZueycs7Ao6EDPrwQWnx4oKzZ/H+PZfCjx49Sk9P/ylqmIRQ771GKyuvrls3Jjm535cvrd+8gZISWrRAs2Zo3hw9e0JDo/vZs3NKS7tra290c9tZx760tbX8/BAeDgcHoWgXOW3btr11K/LcuYtXr/pWVPSgWo44iI3FgwfYy9tzu7gY27fj7l2h9T5u3JCoqLG5uanq6rHz54/q1g0vX+LlSxw4gBcvkJaGJk1gYvLp2rXosrIkoNLPr7OxsbO8vCwnMYOaGuh0KClVGxZy//4js2fvKSwcqKExbPfuHUym7cOH4PwrL4eNDaytMXiwU0TEuuJiRRWVQ25udfrSZWVle/XqpaCASZPqzeYENTW1y5f3BwRsTUtTYbOPEfL7+05nZGDNGly/zuuVrlmDuXO/7guqOy1aNFdRuVVa+grIU1UtZLHUw8Px5AmKi9GmDSwt0bYtLC2xY8fc48eflpUx27bt+ehRnMQNUAR42ZQcIiMj5eVLFRT8//778t27XKaAmEzm3r2Rs2YtffjwoVB6zMkh6urUeGNyhcd5yC9fiLY2efJE1HIIoXRqtKSENGlC4uJ4LR8cTISevOTdu3cREbvu3r3766nKSvLkCQkMvK+s7MeZu5OTc7KyymvblpiaElNToqVFNDSIggIBiKws0dAgGhrExISYmpLWrYmtLVFR6QdkAgRIUFGZ4+JCli4lp06Rn5Yjo6NPenhM2rQpnMnjMmlttGxJrvMd919U8HiDsVikfXsB/UH4hdqpUTc3smIFr4WfPSP6+vw5dtXKjRu3HB2H9O8/5nvvpNxcEh9PNm4k3t7E1pbQaLZADOCsqBhy5MgRfruQrhHWRGRkpLLyClPT9kIJ/8gjnp68uiOKAd6tzqZNxMlJpFq+QqEhnDaNeHvzWri8nBgYiGlw8D1VVVUtWnRWUlresOGc7t3dqyvGYJC8PJKXR1JSyLt35OlTcv8+cXT0ptEuA0RefuPKlaFi0xwWRkaMEFtvtcD7DZaURAwNBVwi5QsKDWFMDGnW7L/sbLXi6UmCgkQpqBqaNOkIHACcNTRGXed/VCU1hDURGRnZrVs3MWfju3GDWFhIRFRPws9DgckkVlbk6FGRyiGEOkN47x7R0+PVa44QsmULGThQlIKqp6ysLCoq6tSpU/y+saWlpdnY9NbTs+3Va1iZcEf1NZKfTzQ0JCKqJ+HzBpswgUybJjotX6HKEHKmQK5e5bX8u3dER0eQLYB1JzHxjr6+hby86rRpiwWoLjWENUFVYt7WrSVlpoivh8KtW+Svv/h2o+cXSgwhg0GsrAjvMy5VVcTEhCRSF2663mWfGDuWhFQbnFms8HX5eXlEX5/cE7GjN1WGcMYM4uPDR3lfX7JsmajE1IrUa/R3Y/x4hIdTLYJ/HBzQtStWr6ZahwgICoKBAYYN47X8oUMwNYW9vSg1/V5MmIDwcLDZVOvgEw0NrFmDqVPrn/JaSU7GkSMICuK1fFoaTp7EVGkCb25IDaEgjB6N2FhkZ1Otg39CQ7F7N16/plqHUHnzBhs3YutWXssTgrVrsWCBKDX9dnTsCDU1XL1KtQ7+GTsWysrYtYtqHUKFxcKECQgNhbY2r1VCQjB+PB/l/yikhlAQ1NUxcCAOHKBaB//o6WH+fEybRrUOIXHlypVly4I9Pf9ZtoyP5AknT0JZGT17ilLZ70g9nQih0bBlC5YsqZcj11/JzMzcv3//rFm3GjaEpyevtb58QVRUvdkDI36khlBAJkzA9u0ghGod/DN9Or58walTVOuoM9u27fHw2BEQYPro0bzmzeN5rxgUhIULRafrt8XLCwkJ+PyZah3807o1vLwwfz7VOupMamqqtXU/H5+CLVs2W1gE8b5Fcv16jBoFSU2cQz1SQyggnTqhYUMkJFCtg39kZbFxI2bPRlkZ1VLqxt69JwsKtgIeTGbQvn28GvbLl1FaioEDRSrt96RBAwwdij17qNYhEMuXIy4OifUpAzEXTp48l5U1jcWaQcjhmJiTPNbKy8OuXZg9W6TS6jdSQyg4vr71cqYIQPfu6NiRj2V2yaRFCzMaLR6AouLVNm3MeKwVGIiFC0GX3vgCMWkSIiLA+l979xkQxbW3AfzZZSlLEeldpGPEGARLVDSCHSICxhJR7AVFrhjFLlYMKPYSUAEVgx1bggpEY0A0oggGxQJYsIErhA7LzvthbvbyEgvbGBbO7xM7O+fMA6v73505c0490zlEp6GBzZvh7w8+n+koEjA1NVRSygIAPG7fvl0TW23fDm/vVrumrlSQ9wPx+fri8mW8ecN0DrFs2YK9e/HwIdM5JODoGKKhcd7U1Nnb+9WCBf5NaXLjBp4+xdixso7Wan35JUxM8MsvTOcQy3ffwdQUO3YwnUMC3buPBOp1dZ3s7KYdObK1KU3Ky7F3LxYtknU0+UYKofg0NeHlhZgYpnOIxdAQCxZg3jymc4grPx8bNmilpsY9f34rLm6nUtNmTly3DosWgSP3M+wyib6PQk5t24bQULm8zAmAojB9Onv16h1FRRkPHvzu4ODQlFa7dmHgQNjYyDqdfCOFUCIzZyIyUl5vUZo/H0+f4vx5pnOITiDA5MlYsgRNeyv4r6wsZGTAz09msdqGMWNw4wYKCpjOIRZra8yejQULmM4hlu3bUVYmWvjqamzf3hpGCckaKYQS6dED7dvL5c1VAJSUsGMHAgNRXc10FBFt3Qo+X+Sx4KGhCAoClyubTG0GlwtfXzm+LW/xYvz5J379lekcIsrNxbp1iI2FgoIIrSIj0auXaJ8X2yZSCCUlpzdX0QYOhKMjwsM/v2fL8eABQkMREyPaO8KTJ0hOxsyZMovVlsyciQMH/re8onzhcrF7N+bNk6fPf3w+/PywZo1oZzjr6hARQSaOaBJSCCVF31z18iXTOcS1ZQu2b6cSEm7evXuX6SyfR78jrFsHa+umNqEo6sWLF+vWVfr7Q0NDluHaDHt72NrizBmmc4hryBB07lw3btzBFStCHz9+zHScz9u4ERoamDWrqftTFJWamrp8+R9ffEE5O8syWWtBCqGk1NUxapS8DpkBYGIiUFf3HjNm78CB68eNa9LYSwaFhkJTEzNmNHX/6urq7t2HODrOPniwr5XVBVlGa1vkesgMgNpa/zNnHq5fb9W37+gXL14wHedTMjOxfTv27xdhheEhQ8aNGBGzefPh169HUfI460ezI4VQCvz98dNPcnlzFYCcnJzycrXa2gPFxceSk3NKSkqYTvRRmZnYuRMHDojwjnDixMmcnP7FxecEgt9WrVony3Rti48PsrPl+PabzMxMilpHUaN5vEkpKS13XoyaGkyciM2b0aFDU5sUFhbevVvO40XV1+8tLGTl5+fLMmArQQqhFHTtCn19XLrEdA6xqKqqslg8AICAosqbeB9C8xO+I5iaitCqro5fX68MAFAUyOno3hZJSQmTJiEykukc4tLW1gDuAjUqKil2drZMx/moVatgbY0JE0RooqamJhAUA/WAQCB4o66uLrN0rQcphNIhv2eKLC0tx4zppqvbQ0nJqWfPKaqqqkwn+rBVq2BpCV9f0Vp9952PmloClztJT89t9er5sonWRs2YgYMH5WnISUOnTu3t2XO5gUF/Lndwt249mY7zYdevIzYWe7kMdPcAACAASURBVPaI1qp9+/aurhMUFZ309JwCArz19fVlk65VIbcWS8e4cQgORmEhTEyYjiK6HTvW/fjj0mfPOC4uSq9etcSZeel3hMxMkRvyeOos1pULF/7q3NmIvCNIl6UlunXDiRMifzppCWxtbdPTzwEYPhw7d2J+y/uMVFkJPz/s3AkDA9Ea1tfj7t05iYmz+vWjOGTyiKYh3wilQ00NY8Zg/36mc4hLVVXV3l5p2rSWuCxDZSUmTcKOHSK/IwBYuxazZ3MGDOhKqqAsyO+JEKHt27FhA16/ZjrHv/zwA3r3ho+PyA0PHYKJCVxdFUgVbDpSCKVm1izs2yevQ2Zoy5YhKQnp6Uzn+P+Cg9GrF0aNErlhQQHOnCGT7svQt9+ioADZ2UznkAB9BS4khOkc/19SEs6dw5YtIjesr0doKFatkkGmVo0UQqn58ksYG+P8eTme3F5dHaGhmDu3BU0al5KChARsbdL0wo2tXIm5c6GtLe1MxD84HEydishIii/PazqEhODcOWRkMJ3jH6WlmDoVUVHQ0hK5bUwMOnRAv34yiNWqtcXvzpWVlTU1NbLomaKWjB59o337uoiIlcOHD5LFIWhaYvwXaZrx4xEVhZgYTJkioyM0SX19fUJCwvPnvIiIUZGR4vy6Dx/i0iXs3CmDcEQD7drFr10bGh/PGTdu+Pbta5mOI4527bByJQIDce2aCHfmyEJGRkZWVtaZMy4jRlgPHSpy87o6bNggx/c0M4ixQpibm0tRlL29/QefraioyM/PZ7PZ1tbWUh/Q37Vr16KiIra0l6QTCAR8voDD4ZSXY+bMKbK7D6G8vDwpKamfbD71sVjYuhXu7vD2Rvv2sjhCk3h4TPzjD9PKSjM1tUEuLlcAkYeAr1iB+fPRrqlLthHiqK+v37gxVCC4UVys8vPPXjNn/tW5c2emQ4lj+nREReHECXz3HWMZYmJ+XrAgpqRkBIs1LiVlDyDylDAxMbCxgYuLLNK1cgwUwoqKCnd398LCQhaLZWRkdOHChUZ3uvz2229eXl4WFhY1NTXv37+Pi4tzdXWVYoCqqqq//vrLRB7HdwIAhg4dWlVVJbv+HR3x7bdYuxabN8vuIJ/C5/Nv384rL48DwGK9TEtLGzx4sEg93L2La9fkdS11OVJXV8diqQIqAAQCk5Y8G8OnsdnYvBmTJsHDg7Fp2XfsOMTjHQL0gc4xMT/36ydaIayrQ2goDh6UUbpWjoFrhD/99JNAILh///79+/fZbPbevXsb7dCjR4+3b9/euXMnJydn/vz5gaKuMkBIbN06HD6M+/eZOTqHwwFqgDdAnaLibTMzM1F7WLkSwcFQU5NFOuJ/VFRUBg1y0tHxVVCYr6Nzv0ePHkwnEl///ujeHZs2MRbA2NgIuAtAWTnTykrkj+n798PeHn37yiBZG8BAITx69OjkyZM5HI6CgsKUKVOOHj3aaAc1NTXheUVLS8t6uR6IKZ/09LBsGQICmDn6q1cAthkYeJuY9Fq06NtOnTqJ1DwjAxkZIsxHSkji8OGdv/wyb9o0j169LioqKjIdRyKbN2P7djx9ysChKQps9lodnc2Ght37978RFDRbpOa1tfjxRzJYVHwMnBp9+vSppaUl/bOlpeWzZ8/+vU9NTc2qVatKS0v//PPPXbt2fayr2tra169fJyUl0Q8VFBR69erFJSvOScOcOdi/HwkJGDmyWY9bVYWRIxEU5BIcnCpeD8uWYflysu5g8+nRo4etLSwt8fYt5Pp2TTMz+Ptj6VLExTX3oZcvB49nXFiYqKwsTvN9++DggJ4tdIYcOSCTQnjp0qXDhw832qigoBAdHQ2gsrJSRUWF3qiiolJeXv7BTrS0tDgcTmlpaVZW1oABAz64T2Fh4V9//bVhwwbhlpCQEEdHx0/Hk/fp2CmKqqqqKisrA/Cxv55UrF+vEBCg0qdPxT8vV5NIEkkgwIQJXBsbyt+/uqxMnB7S0xVyclTi4irEa95sqqurFRQUxPsKJdMXXTwKCnB3V/npJ8F//lMr62PJ9NefMwfOzmoXL1b37i3CiajKyko+ny/2+LtTpzhxccopKZW1tVSt6H+/mhqEhqofPlxVVtaiT55VVVXx+fwysf5nCgSCyspKMdoKBAJlZeXP/keTSSE0Nzcf+q/Bv8J/JQYGBjwePcszeDyeoaHhv3tQVlYODg4GMHr0aGdn5ylTpmh8aCk5CwsLNze3OBE/v7GYHSItMRaLxeVyhX+QD/5lpMLDA7Gx+OknjeXLRWsodqTgYJSU4PhxKCuLeZItNBRr1kBHp6UvPKioqCh2IYQsX3SxBQXBywtLlyqLtGCyeGT362toYONGLFmieusWml7X2Gw2l8sVrxCmpWHhQqSkwNJSzNmxY2LQrRv692+hswQLcblcDocj3mvHZrNVVVXFaCsQCJpycU0mhdDOzs7Ozu5jzzo7O6elpQ0bNgxAamqq8ycXjmzXrh2fzyfrBjAlIgLdumHCBJiby/xYMTE4cQLp6RDv7BCApCS8eCGXU1+2Ao6OMDBAYiLc3ZmOIplx47B3bzPdSvv0KUaNwoEDcHAQs4fqaoSF4dQpqcZqexi4RhgQEDB8+PDOnTuzWKxt27adO3eO3m5ra7t//34XF5cDBw5UVlZaW1uXlJRERET4+Phoamo2f04CgJkZ5szB4sX4+WfZHujaNSxahN9+g56e+J2sWYPVq0FmWGSKvz9275b7QshiYds2uLvDxwcyfeMpK8OIEViyBB4e4neydy+6d0f37tKL1SYxMGq0d+/e8fHxJ0+ePH78+JEjR/r+M+B3xIgR9MzInTt3zsnJ2bFjx9mzZydPnvzvy42tm0Ag0GjAiOnFIIKDceMGrlyR4SHy8jBmDH7+GZLcjX3hAng8jBkjvViEiMaORUYGHj1iOofEHB0xbBjWr5fhIQQC+Pqie3eJxmZXV2PTJjJYVAqY+fA8dOjQf19E3PTPLTw9e/bs2YbHP1EUVV5enpmZSZ9eZvyKJpeLsDDMnYvMTJl82eLxMHw4Vq6Em5v4nVAUVq/GmjUiXNchpE5ZGRMnIioKYWFMR5FYaCgcHDBtGmxls2rvwoX/vRwuid278fXX6NpVSpnaMPK2wbDCwkJ3d3cDAwNjY+MZDe59U1JSUlFRUVFRURb7ipn0jBoFExOsW/fo999/l+40rXV1GD0aHh6YNUuifk6fRn09vLykFIsQ15w5iI2V19V6G9LXx8KFCAwsSU1NFQ7uk5boaFy4gIQESDIPY0UFwsOxYoX0YrVh5HIKaupr4u/F1/BlMg13I8Yaxh62/++CQHh4uK6uLn0zZX5+vnD7kSNHDAwMAHTp0qV///7NkO3THBx2rl17Ztu2Trq6izIyLrWT0iSe8+ZBSQk//ihRJwIB1qzBhg0Mz5hMADA3h7Mzjh6Fnx/TUSTm6pq1ZMmk1NR+qqrXzp7d26OHdC7EXbuGpUtx9ao4i0s0tHs3+vXDl19KJVRbRwohbr28NSlhUvMcS1lB+e3Ct+2U/1dFunfvHhUVlZaWZmJi0nAK8mfPntE3zXzw9pLmd/RojECQXlLCqaoKP3v2nK/veMn73LQJ6em4dg0SDriPj4eSEoYNkzwRIQX+/lizpjUUwo0b99TX7ywr611Wdnflys2JiVKYx/PxY4wejSNHJD3jWlGBiAhcuiR5IgIghRBAH7M+L4JeNM83Qh1VnYZVEICDg0NZWVlYWFjPnj1DGiwPunjxYlGnFpMpRUUFoAzQ4vOLq6psJOkqPHzXjh0xgHp1dXhGhrO6mHdPAUBJScmjR09Wr+60e7cq+TrYQgwbhoAA3LqFT94YJQfU1FRYrFKKAlBaUyPRTEWPHj0KCFjN45W9fj1vzRq3j0wQIoKdOzFgALp0kbQfgkYKIQCYaDC2EsWCBQuCgoLGj5fCFyyZ2rVr7ZQp3wgE6lpaHVas2KCjA29vcfrJzs7+8cdf3r1LA14aGY02M7shdqTU1DQfn4DKyu41NTctLE4AlmJ3RUgRm41Zs7B7t9yv/rFu3YLff/eurNxBUUV//XUsLAwLF4p5+n3o0Al5eVsBfS53vLt7J8BYvEgCgcDXNyA5OZ3HUz1xYjdAKqF0kMEyDDM1Nb18+XJhYSGPx7t8+XKLnWF8+PDBL1/eKSi4nJv787lzCosWYe5cccZEZGcXlJX1BBQB8/p6SpKpEpYsiXjz5khZ2d7a2tDQ0D1i90NI3ZQpSEiAtIeYNDdTU9O8vJtZWTGvX9/MyLA4exbffot370Tup7q6+v17RaAXYKms/E1OTo7YkU6fTjh3Tunt2ww+/8CKFfPF7odohBRChm3ZskVTU9PLy+ubb77Zv38/ABaL5enp2QLn0KJnOQLQvTvu3MH793Bywr17TW1eW4tt2xAY2EdRMYHN3qemtszZ2U6S5ZGVlBSBKgAsVqXYU7IRsqCri2+/RXQ00zmkQV9fn8VimZnhyhU4O6NbN6SKMht8bi4mTlQpL1dksY4AySoqlz87GfInvHjxurKS/hZoVVJSKnY/RCOkEDJMS0tr27ZtN2/ezMrKio+PV1BQYLPZCQkJpqamTEf7FA0NxMUhOBgDBmDbts/sLBDg+HHY2+P8efz2m/bjx7+Eh1f99JPDmTMSvVOuXr2MzZ6io+NpYbFx+fJ5knRFSJ2/P/bsQWuaG5HDQUgIdu2Cjw9CQj7/qxUXIzAQ/frByQn5+ccCAx9MnHguKSlWR0dH7Aze3t+qqOxgs7dqaY318xsldj9EI+QaISG+iRPRowfGjEFqKqKiPjwfVVISfvgBXC5iY+HiQm8zDAqSwlKH8fEO/v43Fy9+Y2RkJMk3S0IWevaElhYuXcK/Zs6Qbx4euHXrv3PoxMZCW/sD+1RUYOdObNqE779Hbi7atwegu2XLGsmPXl5upqqaGB5+8auv/tO7d2/JOyRo5O2DkIi9PW7cgJERevTAnTuoqKjIysqib/z480+4uiIgAMuW4fp1YRWUjgcPcOwYQkI4JiYmpAq2TLNnY/dupkPIgKkprlyBk9MHTpPy+YiMhK0tMjJw8ya2baOroNQEBWHlSgN//4mkCjZRYWFhZWXlZ3cj7yCEpFRUsG0bQkMxePBDExOXkSOjraz6u7tneHtj7FhkZ+O776R/0AULsHQpJDjJRMjcuHFIT0eDWSJaD/o06c6d8PaGk9MP5uZfm5k5hYYmdeuG48dx/jyOHYOFhZQP+ssvKCiQdA4mucPn893dfe/dezhihN/t23dEahsWtsvJyffWrc+P2iKnRgnp8PbGoUO7EhI2Aa5A1sOHYbm5h1Vls0RaSgpyc3H6tEw6J6SFy8WECYiMRGgo01Fkw8MDu3enjh37ls/PAt6vWjXo/PlbgwfL5Fh8PhYtwubNEHcJS3kVH3/syhULPr/Ty5ervLwWbtqUVFGBRssX19Sg0bc+gQClpdi5c39Fxc27d9mfnceYFEJCanR0FFmsaooCUG1nx5FRFayvx3/+gy1bJJqnkWge/v7o3RurVkFFhekosqGo+E5FxbK8HEB7LS1q0CAKkMnMDnv2wNAQw4fLou8W7c2bd9XV9C3CesXFZcePQ02t8f99ZWU0erdhs6GlBTabAvgqKp//7EAKISE1q1YFJiV5V1VFKSo+DQ+Pk9FRoqL+OzqfaPmsrODoiBMnWu1qya6urvr66wGBktJjH5/BMlor5v17rF+Py5dl0XdLN3asd0SEx+vXbzU0FqxYMWnBAhHa6ugsWLq0r51dPGD96T3baCHMysp6/fo10ynE9PfffzMd4cPMzMweP77+6NEja2trRdmcwSkrw5o1OH9eFn0TMuHvj40bW20hVFdXz87+7dy5c6amQ/r06SOjo6xeDR+fNjqhmomJSXZ2srOz8/r1c8eNGydS22nTfL/77luFJsxl3BYLYZ8+fVYwtHhJdTWKimBmJmk/LfYuQw6HY2pqKqMqCGDtWri7o1s3GXVPSJ+HBwIDkZEBJyemo8iGqqqqh4cHlyvRZKSf8Pgx4uNFmLmi9dHW1tbU1LQVa55yDQ2NpkzX1RYL4dGjRxk8upMTQkMxaBCDEeRVfj6io5GVxXQOQhRsNmbMwN69iIpiOop8mj8fixZBV5fpHK0auX2iuX333bOZMxfNmhVMr0FINN2iRQgKgpER0zkIEXl5vT14cGaXLkP27m0Vs641o5QU3L+POXOYztHakULYrOrq6nbv9s7Pd4mM7NOvnw+fz2c6kdy4fh03biAwkOkchOhmzfKvq3O/dy9u6dIz165dYzqO3KAHSG/eDGVlpqO0dqQQNquCgoKaGlvgW4oaUVVlk5eXx3Qi+SAQIDAQYWGNB0kTcuHJk6cUNQLQLSnx/vPPTKbjyA16gLSnJ9M52gBSCJuVmZkZm30fyAH+YrHud+jQgelE8uHwYSgoYMwYpnMQYund20lZeSNwTV09ctCgb5iOIx/KyrB2LcLDmc7RNsh3ISwtLX0nxvpgzFFRUTl3bl+/fit1dUMmTNinItltxrW1tSkpKdLKJi2JiYnS7bCqCitWYNMmMddEbZnu3bsn3kViiqIuXrwo9TwyFRu7NSSE6+JyomPHH7tIfBNAUlJSS7umcPPmzeLiYun2SQ+Qbk1DbcvLy9+/fy9225qaGjEavnv3Lj09/fP7UfJs9uzZHTt2ZDqFOG7epCwsqLo6iTp58OCBjY2NlBJJjaqqanl5uRQ7XLWK+v57KfbXIsyZM2fLli1iNHz79q2urq7U8zQDPp+ysqLS0iTtx9zcPC8vTxqJpMbT0/PEiRNS7PDJE0pXl3r5UopdMi8kJERPT0+8tqqqqocOHRKj4enTp0eMGPHZ3eT7GyFFUUxHEFP37jA2JrNlfl5hIXbtwoYNTOeQAfn91yseBQXMm4eICKZzyIZ0X82FC/HDD2SAtBQ08XWR70Io1xYsIBcAPm/pUsyaBXNzpnMQ0jBtGq5exZMnTOdo2a5exZ07ZIB0syKFkDGenigrAxlM/kE1NTWRkQfmzt2cmPhy4UKm0xBSoqqKadOwbRvTOVqqvLy8s2fP/ec/b8PCWu005S0TS67Pz4wdO/bs2bOym+JP1l69GlhZaWJlFSte88rKyszMzJa2ROdvv/3Wv39/CRfLzc19U1zcv67uCyWlQ716cVvf0ru5ubmqqqpmos+2V1dXd/369X79+skiVTOoqdG5d29pt27BLJaYA15SU1OdnJwkHGgmXVlZWYaGhvr6+pJ0Ul5enp1dVV/fl8+/17s3lJRa23pLz549e/bsWd++fcVoe/Xq1U6dOonxFy4qKgKQmfmZm3bkuxA+evTowoULDg4OTAdhBkVRT58+7dixI9NB/p/8/HwLqa9J2uoUFxcrKytraGiI0baN/4ULCgrMzc1ltM6DeF6+fKmtrd2ianMLVFtbW1RUZGJiIkbbp0+fmpqaNmX67EZqampUVVUHDBjw6d3kuxASBEEQhIRa2xkngiAIghAJKYQEQRBEm0YKIUEQBNGmkUJIEARBtGkKISEhTGcQR15eXlZW1tN/FBcXGxsbMx2qmdy/f//u3bv6+vpKSkr0lvz8/IyMjHbt2qkytzrDH3/8oaamRgegKOrq1at1dXU6OjpM5Wmx7t69m5ubK/ynW1NToyvKoqvv3r1LS0szMjJSVGxtw+s/rb6+/tq1a8bGxmIMHZSR58+fP3/+XDimn05oZGTE4bTFBc9pubm5jx49+th9QXFxcS9evLCxsaEfFhQU3Lp1S1lZ+c6dOx06dGCxWK9fv05PT+dwOJqamg0bvn37dvny5UOHDm3UYXZ2dl1dXbt27eiHPB4vOzvbxMSkpKTkxo0bwhH1RUVF169f19PTU/7YilZizN7WEgQFBRkZGbn+Y+rUqUwnaj7Tp09ns9m7du0SbhkyZAibzT579iyDqczMzH799VeKovh8/tSpU52cnIqKihjM02K5uLjY2dkJ/+mGhYWJ1Hzp0qUsFis2NlZG8VqskpISAM+fP2c6yP+EhoZ6eHgIH5aWlgJ49uwZg5EY17VrV0VFxVevXn3w2R9++CEiIkL4MDQ0lM1mT5s2DUBZWRlFUf7+/mw2+9/T8D558sTW1vbfHQ4dOjQ8PFz48OzZs/Ts07///ru2tja9saCgwMbGJjg4+BOx5fiTy5AhQ6Kj2+iC1998801sbKy/vz+AwsLC+/fvW1tbMx0KAGpra319fd+8eZOSkiL8mEY0Mm/ePPq1E5VAIDh06NC8efOio6MnTpwo9WAEIYmMjIyioqKRI0ceOnRoYdNmhHJxcTl//jz9c3V19enTp7/++mspRnrw4MHgwYMDAgI+nYdcI5RLPXv2rKqqys7OBhATE+Pr69sSzhdVVlZ6enpWVlY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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/examples/wannier90/index.html b/dev/examples/wannier90/index.html index f22aa6a0e2..711be5cc97 100644 --- a/dev/examples/wannier90/index.html +++ b/dev/examples/wannier90/index.html @@ -17,12 +17,12 @@ nbandsalg = AdaptiveBands(basis.model; n_bands_converge=15) scfres = self_consistent_field(basis; nbandsalg, tol=1e-5);
    n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime
     ---   ---------------   ---------   ---------   ----   ------
    -  1   -11.14942108834                   -0.67    8.4
    -  2   -11.15007170866       -3.19       -1.40    1.0    201ms
    -  3   -11.15010723377       -4.45       -2.76    3.6    288ms
    -  4   -11.15010941033       -5.66       -3.30    4.4    360ms
    -  5   -11.15010943267       -7.65       -4.18    3.0    250ms
    -  6   -11.15010943373       -8.97       -5.05    4.0    334ms

    Plot bandstructure of the system

    plot_bandstructure(scfres; kline_density=10)
    Example block output

    Now we use the run_wannier90 routine to generate all files needed by wannier90 and to perform the wannierization procedure. In Wannier90's convention, all files are named with the same prefix and only differ by their extensions. By default all generated input and output files are stored in the subfolder "wannier90" under the prefix "wannier" (i.e. "wannier90/wannier.win", "wannier90/wannier.wout", etc.). A different file prefix can be given with the keyword argument fileprefix as shown below.

    We now solve for 5 MLWF using wannier90:

    using wannier90_jll  # Needed to make run_wannier90 available
    +  1   -11.14941534616                   -0.67    8.6
    +  2   -11.15007148266       -3.18       -1.40    1.0    393ms
    +  3   -11.15010722723       -4.45       -2.77    3.2    496ms
    +  4   -11.15010941067       -5.66       -3.30    5.0    796ms
    +  5   -11.15010943266       -7.66       -4.13    2.6    527ms
    +  6   -11.15010943369       -8.99       -5.03    3.6    591ms

    Plot bandstructure of the system

    plot_bandstructure(scfres; kline_density=10)
    Example block output

    Now we use the run_wannier90 routine to generate all files needed by wannier90 and to perform the wannierization procedure. In Wannier90's convention, all files are named with the same prefix and only differ by their extensions. By default all generated input and output files are stored in the subfolder "wannier90" under the prefix "wannier" (i.e. "wannier90/wannier.win", "wannier90/wannier.wout", etc.). A different file prefix can be given with the keyword argument fileprefix as shown below.

    We now solve for 5 MLWF using wannier90:

    using wannier90_jll  # Needed to make run_wannier90 available
     run_wannier90(scfres;
                   fileprefix="wannier/graphene",
                   n_wannier=5,
    @@ -39,4 +39,4 @@
                   wannier_plot_supercell=5,
                   write_xyz=true,
                   translate_home_cell=true,
    -             );

    As can be observed standard optional arguments for the disentanglement can be passed directly to run_wannier90 as keyword arguments.

    (Delete temporary files.)

    rm("wannier", recursive=true)
    + );

    As can be observed standard optional arguments for the disentanglement can be passed directly to run_wannier90 as keyword arguments.

    (Delete temporary files.)

    rm("wannier", recursive=true)
    diff --git a/dev/features/index.html b/dev/features/index.html index ad78fd6376..888ec1afbf 100644 --- a/dev/features/index.html +++ b/dev/features/index.html @@ -1,2 +1,2 @@ -DFTK features · DFTK.jl

    DFTK features

    • Runs out of the box on Linux, macOS and Windows

    Missing a feature? Look for an open issue or create a new one. Want to contribute? See our contributing notes.

    +DFTK features · DFTK.jl

    DFTK features

    • Runs out of the box on Linux, macOS and Windows

    Missing a feature? Look for an open issue or create a new one. Want to contribute? See our contributing notes.

    diff --git a/dev/guide/installation/index.html b/dev/guide/installation/index.html index d474a8e723..cb87ac069d 100644 --- a/dev/guide/installation/index.html +++ b/dev/guide/installation/index.html @@ -2,4 +2,4 @@ Installation · DFTK.jl

    Installation

    In case you don't have a working Julia installation yet, first download the Julia binaries and follow the Julia installation instructions. At least Julia 1.6 is required for DFTK.

    Afterwards you can install DFTK like any other package in Julia. For example run in your Julia REPL terminal:

    import Pkg
     Pkg.add("DFTK")

    which will install the latest DFTK release. Alternatively (if you like to be fully up to date) install the master branch:

    import Pkg
     Pkg.add(name="DFTK", rev="master")

    DFTK is continuously tested on Debian, Ubuntu, mac OS and Windows and should work on these operating systems out of the box.

    That's it. With this you are all set to run the code in the Tutorial or the examples directory.

    DFTK version compatibility

    We follow the usual semantic versioning conventions of Julia. Therefore all DFTK versions with the same minor (e.g. all 0.6.x) should be API compatible, while different minors (e.g. 0.7.y) might have breaking changes. These will also be announced in the release notes.

    While not strictly speaking required to use DFTK it is usually convenient to install a couple of standard packages from the AtomsBase ecosystem to make working with DFT more convenient. Examples are

    You can install these packages using

    import Pkg
    -Pkg.add(["AtomsIO", "AtomsIOPython", "ASEconvert"])
    Python dependencies in Julia

    There are two main packages to use Python dependencies from Julia, namely PythonCall and PyCall. These packages can be used side by side, but some care is needed. By installing AtomsIOPython and ASEconvert you indirectly install PythonCall which these two packages use to manage their third-party Python dependencies. This might cause complications if you plan on using PyCall-based packages (such as PyPlot) In contrast AtomsIO is free of any Python dependencies and can be safely installed in any case.

    Installation for DFTK development

    If you want to contribute to DFTK, see the Developer setup for some additional recommendations on how to setup Julia and DFTK.

    +Pkg.add(["AtomsIO", "AtomsIOPython", "ASEconvert"])
    Python dependencies in Julia

    There are two main packages to use Python dependencies from Julia, namely PythonCall and PyCall. These packages can be used side by side, but some care is needed. By installing AtomsIOPython and ASEconvert you indirectly install PythonCall which these two packages use to manage their third-party Python dependencies. This might cause complications if you plan on using PyCall-based packages (such as PyPlot) In contrast AtomsIO is free of any Python dependencies and can be safely installed in any case.

    Installation for DFTK development

    If you want to contribute to DFTK, see the Developer setup for some additional recommendations on how to setup Julia and DFTK.

    diff --git a/dev/guide/introductory_resources/index.html b/dev/guide/introductory_resources/index.html index ecd10136d5..72952149fc 100644 --- a/dev/guide/introductory_resources/index.html +++ b/dev/guide/introductory_resources/index.html @@ -1,2 +1,2 @@ -Introductory resources · DFTK.jl

    Introductory resources

    This page collects a bunch of articles, lecture notes, textbooks and recordings related to density-functional theory (DFT) and DFTK. Since DFTK aims for an interdisciplinary audience the level and scope of the referenced works varies. They are roughly ordered from beginner to advanced. For a list of articles dealing with novel research aspects achieved using DFTK, see Publications.

    Workshop material and tutorials

    Textbooks

    Recordings

    +Introductory resources · DFTK.jl

    Introductory resources

    This page collects a bunch of articles, lecture notes, textbooks and recordings related to density-functional theory (DFT) and DFTK. Since DFTK aims for an interdisciplinary audience the level and scope of the referenced works varies. They are roughly ordered from beginner to advanced. For a list of articles dealing with novel research aspects achieved using DFTK, see Publications.

    Workshop material and tutorials

    Textbooks

    Recordings

    diff --git a/dev/guide/periodic_problems.ipynb b/dev/guide/periodic_problems.ipynb index 0499418259..ca276cda55 100644 --- a/dev/guide/periodic_problems.ipynb +++ b/dev/guide/periodic_problems.ipynb @@ -278,7 +278,7 @@ { "output_type": "execute_result", "data": { - "text/plain": "PlaneWaveBasis discretization:\n architecture : DFTK.CPU()\n num. mpi processes : 1\n num. julia threads : 1\n num. blas threads : 2\n num. fft threads : 1\n\n Ecut : 300.0 Ha\n fft_size : (320, 1, 1), 320 total points\n kgrid type : Monkhorst-Pack\n kgrid : [1, 1, 1]\n num. irred. kpoints : 1\n\n Discretized Model(custom, 1D):\n lattice (in Bohr) : [20 , 0 , 0 ]\n [0 , 0 , 0 ]\n [0 , 0 , 0 ]\n unit cell volume : 20 Bohr\n \n num. electrons : 0\n spin polarization : none\n temperature : 0 Ha\n \n terms : Kinetic()" + "text/plain": "PlaneWaveBasis discretization:\n architecture : DFTK.CPU()\n num. mpi processes : 1\n num. julia threads : 1\n num. blas threads : 1\n num. fft threads : 1\n\n Ecut : 300.0 Ha\n fft_size : (320, 1, 1), 320 total points\n kgrid type : Monkhorst-Pack\n kgrid : [1, 1, 1]\n num. irred. kpoints : 1\n\n Discretized Model(custom, 1D):\n lattice (in Bohr) : [20 , 0 , 0 ]\n [0 , 0 , 0 ]\n [0 , 0 , 0 ]\n unit cell volume : 20 Bohr\n \n num. electrons : 0\n spin polarization : none\n temperature : 0 Ha\n \n terms : Kinetic()" }, "metadata": {}, "execution_count": 3 @@ -308,7 +308,7 @@ "text": [ "Computing bands along kpath:\n", " Γ -> X\n", - "\rDiagonalising Hamiltonian kblocks: 12%|██ | ETA: 0:00:06\u001b[K\rDiagonalising Hamiltonian kblocks: 100%|████████████████| Time: 0:00:00\u001b[K\n" + "\rDiagonalising Hamiltonian kblocks: 12%|██ | ETA: 0:00:12\u001b[K\rDiagonalising Hamiltonian kblocks: 100%|████████████████| Time: 0:00:01\u001b[K\n" ] }, { @@ -320,274 +320,274 @@ "\n", "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "\n" ], "image/svg+xml": [ "\n", "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "\n" ] }, @@ -665,97 +665,97 @@ "\n", "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n" + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" ], "image/svg+xml": [ "\n", "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n" + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" ] }, "metadata": {}, @@ -792,90 +792,90 @@ "\n", "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "\n" ], "image/svg+xml": [ "\n", "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "\n" ] }, @@ -931,276 +931,276 @@ "text": [ "Computing bands along kpath:\n", " Γ -> X\n", - "\rDiagonalising Hamiltonian kblocks: 75%|████████████ | ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 100%|████████████████| Time: 0:00:00\u001b[K\n" + "\rDiagonalising Hamiltonian kblocks: 56%|█████████ | ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 100%|████████████████| Time: 0:00:00\u001b[K\n" ] }, { "output_type": "execute_result", "data": { "text/plain": "Plot{Plots.GRBackend() n=6}", - "image/png": 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", + "image/png": 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b/dev/guide/periodic_problems/284fdb6f.svg similarity index 91% rename from dev/guide/periodic_problems/d4351026.svg rename to dev/guide/periodic_problems/284fdb6f.svg index a8aaf388e1..7284dfbb39 100644 --- a/dev/guide/periodic_problems/d4351026.svg +++ b/dev/guide/periodic_problems/284fdb6f.svg @@ -1,43 +1,43 @@ - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/guide/periodic_problems/10726071.svg b/dev/guide/periodic_problems/30100640.svg similarity index 77% rename from dev/guide/periodic_problems/10726071.svg rename to dev/guide/periodic_problems/30100640.svg index ba92c10b2b..4f7829ff25 100644 --- a/dev/guide/periodic_problems/10726071.svg +++ b/dev/guide/periodic_problems/30100640.svg @@ -1,130 +1,130 @@ - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/guide/periodic_problems/3e28d2dd.svg b/dev/guide/periodic_problems/5019f0a7.svg similarity index 75% rename from dev/guide/periodic_problems/3e28d2dd.svg rename to dev/guide/periodic_problems/5019f0a7.svg index 3f31dd4c93..9a33104928 100644 --- a/dev/guide/periodic_problems/3e28d2dd.svg +++ b/dev/guide/periodic_problems/5019f0a7.svg @@ -1,134 +1,135 @@ - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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Ha @@ -50,10 +50,10 @@ using UnitfulAtomic using Plots -plot_bandstructure(basis; n_bands=6, kline_density=100)Example block output
    Selection of k-point grids in `PlaneWaveBasis` construction

    You might wonder why we only selected a single $k$-point (clearly a very crude and inaccurate approximation). In this example the kgrid parameter specified in the construction of the PlaneWaveBasis is not actually used for plotting the bands. It is only used when solving more involved models like density-functional theory (DFT) where the Hamiltonian is non-linear. In these cases before plotting the bands the self-consistent field equations (SCF) need to be solved first. This is typically done on a different $k$-point grid than the grid used for the bands later on. In our case we don't need this extra step and therefore the kgrid value passed to PlaneWaveBasis is actually arbitrary.

    Adding potentials

    So far so good. But free electrons are actually a little boring, so let's add a potential interacting with the electrons.

    • The modified problem we will look at consists of diagonalizing

      \[H_k = \frac12 (-i \nabla + k)^2 + V\]

      for all $k \in B$ with a periodic potential $V$ interacting with the electrons.

    • A number of "standard" potentials are readily implemented in DFTK and can be assembled using the terms kwarg of the model. This allows to seamlessly construct

    We will use ElementGaussian, which is an analytic potential describing a Gaussian interaction with the electrons to DFTK. See Custom potential for how to create a custom potential.

    A single potential looks like:

    using Plots
    +plot_bandstructure(basis; n_bands=6, kline_density=100)
    Example block output
    Selection of k-point grids in `PlaneWaveBasis` construction

    You might wonder why we only selected a single $k$-point (clearly a very crude and inaccurate approximation). In this example the kgrid parameter specified in the construction of the PlaneWaveBasis is not actually used for plotting the bands. It is only used when solving more involved models like density-functional theory (DFT) where the Hamiltonian is non-linear. In these cases before plotting the bands the self-consistent field equations (SCF) need to be solved first. This is typically done on a different $k$-point grid than the grid used for the bands later on. In our case we don't need this extra step and therefore the kgrid value passed to PlaneWaveBasis is actually arbitrary.

    Adding potentials

    So far so good. But free electrons are actually a little boring, so let's add a potential interacting with the electrons.

    • The modified problem we will look at consists of diagonalizing

      \[H_k = \frac12 (-i \nabla + k)^2 + V\]

      for all $k \in B$ with a periodic potential $V$ interacting with the electrons.

    • A number of "standard" potentials are readily implemented in DFTK and can be assembled using the terms kwarg of the model. This allows to seamlessly construct

    We will use ElementGaussian, which is an analytic potential describing a Gaussian interaction with the electrons to DFTK. See Custom potential for how to create a custom potential.

    A single potential looks like:

    using Plots
     using LinearAlgebra
     nucleus = ElementGaussian(0.3, 10.0)
    -plot(r -> DFTK.local_potential_real(nucleus, norm(r)), xlims=(-50, 50))
    Example block output

    With this element at hand we can easily construct a setting where two potentials of this form are located at positions $20$ and $80$ inside the lattice $[0, 100]$:

    using LinearAlgebra
    +plot(r -> DFTK.local_potential_real(nucleus, norm(r)), xlims=(-50, 50))
    Example block output

    With this element at hand we can easily construct a setting where two potentials of this form are located at positions $20$ and $80$ inside the lattice $[0, 100]$:

    using LinearAlgebra
     
     # Define the 1D lattice [0, 100]
     lattice = diagm([100., 0, 0])
    @@ -73,7 +73,7 @@
     potential = DFTK.total_local_potential(ham)[:, 1, 1]
     rvecs = collect(r_vectors_cart(basis))[:, 1, 1]  # slice along the x axis
     x = [r[1] for r in rvecs]                        # only keep the x coordinate
    -plot(x, potential, label="", xlabel="x", ylabel="V(x)")
    Example block output

    This potential is the sum of two "atomic" potentials (the two "Gaussian" elements). Due to the periodic setting we are considering interactions naturally also occur across the unit cell boundary (i.e. wrapping from 100 over to 0). The required periodization of the atomic potential is automatically taken care, such that the potential is smooth across the cell boundary at 100/0.

    With this setup, let's look at the bands:

    using Unitful
    +plot(x, potential, label="", xlabel="x", ylabel="V(x)")
    Example block output

    This potential is the sum of two "atomic" potentials (the two "Gaussian" elements). Due to the periodic setting we are considering interactions naturally also occur across the unit cell boundary (i.e. wrapping from 100 over to 0). The required periodization of the atomic potential is automatically taken care, such that the potential is smooth across the cell boundary at 100/0.

    With this setup, let's look at the bands:

    using Unitful
     using UnitfulAtomic
     
    -plot_bandstructure(basis; n_bands=6, kline_density=500)
    Example block output

    The bands are noticeably different.

    • The bands no longer overlap, meaning that the spectrum of $H$ is no longer continuous but has gaps.

    • The two lowest bands are almost flat. This is because they represent two tightly bound and localized electrons inside the two Gaussians.

    • The higher the bands are in energy, the more free-electron-like they are. In other words the higher the kinetic energy of the electrons, the less they feel the effect of the two Gaussian potentials. As it turns out the curvature of the bands, (the degree to which they are free-electron-like) is highly related to the delocalization of electrons in these bands: The more curved the more delocalized. In some sense "free electrons" correspond to perfect delocalization.

    • 1Notice that block-diagonal is a bit an abuse of terms here, since the Hamiltonian is not a matrix but an operator and the number of blocks is infinite. The mathematically precise term is that the Bloch transform reveals the fibers of the Hamiltonian.
    +plot_bandstructure(basis; n_bands=6, kline_density=500)Example block output

    The bands are noticeably different.

    • The bands no longer overlap, meaning that the spectrum of $H$ is no longer continuous but has gaps.

    • The two lowest bands are almost flat. This is because they represent two tightly bound and localized electrons inside the two Gaussians.

    • The higher the bands are in energy, the more free-electron-like they are. In other words the higher the kinetic energy of the electrons, the less they feel the effect of the two Gaussian potentials. As it turns out the curvature of the bands, (the degree to which they are free-electron-like) is highly related to the delocalization of electrons in these bands: The more curved the more delocalized. In some sense "free electrons" correspond to perfect delocalization.

    • 1Notice that block-diagonal is a bit an abuse of terms here, since the Hamiltonian is not a matrix but an operator and the number of blocks is infinite. The mathematically precise term is that the Bloch transform reveals the fibers of the Hamiltonian.
    diff --git a/dev/guide/tutorial.ipynb b/dev/guide/tutorial.ipynb index 291a899b33..89c2ba5a2f 100644 --- a/dev/guide/tutorial.ipynb +++ b/dev/guide/tutorial.ipynb @@ -75,14 +75,14 @@ "text": [ "n Energy log10(ΔE) log10(Δρ) Diag Δtime\n", "--- --------------- --------- --------- ---- ------\n", - " 1 -7.900220655223 -0.70 4.5 \n", - " 2 -7.904964763088 -2.32 -1.52 1.0 413ms\n", - " 3 -7.905173304115 -3.68 -2.53 1.4 60.7ms\n", - " 4 -7.905210600218 -4.43 -2.84 2.8 51.2ms\n", - " 5 -7.905211151673 -6.26 -3.00 1.1 83.3ms\n", - " 6 -7.905211520708 -6.43 -4.56 1.0 29.5ms\n", - " 7 -7.905211531212 -7.98 -4.69 2.6 72.5ms\n", - " 8 -7.905211531390 -9.75 -5.30 1.0 54.1ms\n" + " 1 -7.900367536290 -0.70 4.5 \n", + " 2 -7.904991049838 -2.34 -1.52 1.0 749ms\n", + " 3 -7.905177781031 -3.73 -2.53 1.2 106ms\n", + " 4 -7.905210653071 -4.48 -2.85 2.8 102ms\n", + " 5 -7.905211149178 -6.30 -2.99 1.0 164ms\n", + " 6 -7.905211518754 -6.43 -4.47 1.0 65.4ms\n", + " 7 -7.905211531210 -7.90 -4.70 2.8 147ms\n", + " 8 -7.905211531377 -9.78 -5.03 1.0 100ms\n" ] } ], @@ -123,7 +123,7 @@ { "output_type": "execute_result", "data": { - "text/plain": "Energy breakdown (in Ha):\n Kinetic 3.1020970 \n AtomicLocal -2.1987861\n AtomicNonlocal 1.7296101 \n Ewald -8.3979253\n PspCorrection -0.2946254\n Hartree 0.5530397 \n Xc -2.3986214\n\n total -7.905211531390" + "text/plain": "Energy breakdown (in Ha):\n Kinetic 3.1020949 \n AtomicLocal -2.1987810\n AtomicNonlocal 1.7296081 \n Ewald -8.3979253\n PspCorrection -0.2946254\n Hartree 0.5530379 \n Xc -2.3986208\n\n total -7.905211531377" }, "metadata": {}, "execution_count": 3 @@ -148,7 +148,7 @@ { "output_type": "execute_result", "data": { - "text/plain": "7×8 Matrix{Float64}:\n -0.176942 -0.147441 -0.0911693 … -0.101219 -0.0239771 -0.0184081\n 0.261073 0.116914 0.004825 0.0611642 -0.0239771 -0.0184081\n 0.261073 0.232989 0.216733 0.121636 0.155531 0.117747\n 0.261073 0.232989 0.216733 0.212134 0.155531 0.117747\n 0.354532 0.335109 0.317102 0.350436 0.285692 0.417258\n 0.354532 0.389828 0.384601 … 0.436926 0.285692 0.417262\n 0.354532 0.389828 0.384601 0.449329 0.627584 0.443806" + "text/plain": "7×8 Matrix{Float64}:\n -0.176941 -0.14744 -0.0911688 … -0.101219 -0.0239766 -0.0184075\n 0.261074 0.116915 0.00482539 0.0611648 -0.0239766 -0.0184075\n 0.261074 0.23299 0.216734 0.121636 0.155532 0.117748\n 0.261074 0.23299 0.216734 0.212135 0.155532 0.117748\n 0.354532 0.33511 0.317103 0.350436 0.285692 0.417258\n 0.354532 0.389829 0.384601 … 0.436926 0.285692 0.417411\n 0.354533 0.389829 0.384601 0.449307 0.627598 0.443806" }, "metadata": {}, "execution_count": 4 @@ -218,143 +218,143 @@ "output_type": "execute_result", "data": { "text/plain": "Plot{Plots.GRBackend() n=1}", - "image/png": 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", + "image/png": 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-> L -> W -> X\n", - "\rDiagonalising Hamiltonian kblocks: 7%|█▏ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 43%|██████▉ | ETA: 0:00:01\u001b[K\rDiagonalising Hamiltonian kblocks: 79%|████████████▋ | ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 93%|██████████████▉ | ETA: 0:00:00\u001b[K\rDiagonalising Hamiltonian kblocks: 100%|████████████████| Time: 0:00:01\u001b[K\n" + "\rDiagonalising Hamiltonian kblocks: 7%|█▏ | ETA: 0:00:02\u001b[K\rDiagonalising Hamiltonian kblocks: 100%|████████████████| Time: 0:00:01\u001b[K\n" ] }, { "output_type": "execute_result", "data": { "text/plain": "Plot{Plots.GRBackend() n=44}", - "image/png": 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", + "image/png": 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a/dev/guide/tutorial/a20a7a08.svg +++ b/dev/guide/tutorial/aee795ac.svg @@ -1,220 +1,220 @@ - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/guide/tutorial/e84aacd3.svg b/dev/guide/tutorial/e84aacd3.svg new file mode 100644 index 0000000000..2684fe0e09 --- /dev/null +++ b/dev/guide/tutorial/e84aacd3.svg @@ -0,0 +1,67 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/guide/tutorial/index.html b/dev/guide/tutorial/index.html index a29b76045d..058082e0e7 100644 --- a/dev/guide/tutorial/index.html +++ b/dev/guide/tutorial/index.html @@ -27,30 +27,30 @@ # 3. Run the SCF procedure to obtain the ground state scfres = self_consistent_field(basis, tol=1e-5);
    n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime
     ---   ---------------   ---------   ---------   ----   ------
    -  1   -7.900362778011                   -0.70    4.8
    -  2   -7.904987487715       -2.33       -1.52    1.0   23.8ms
    -  3   -7.905175188823       -3.73       -2.53    1.2   24.9ms
    -  4   -7.905210712253       -4.45       -2.83    2.6   34.0ms
    -  5   -7.905211098171       -6.41       -2.95    1.0    155ms
    -  6   -7.905211521236       -6.37       -4.69    1.0   23.7ms
    -  7   -7.905211531067       -8.01       -4.55    3.2   38.2ms
    -  8   -7.905211531367       -9.52       -5.02    1.0   24.2ms

    That's it! Now you can get various quantities from the result of the SCF. For instance, the different components of the energy:

    scfres.energies
    Energy breakdown (in Ha):
    -    Kinetic             3.1020934 
    -    AtomicLocal         -2.1987772
    -    AtomicNonlocal      1.7296065 
    +  1   -7.900343262652                   -0.70    4.8
    +  2   -7.904995023265       -2.33       -1.52    1.0   50.5ms
    +  3   -7.905175841552       -3.74       -2.53    1.1   49.8ms
    +  4   -7.905210638857       -4.46       -2.83    2.9   84.7ms
    +  5   -7.905211094360       -6.34       -2.96    1.1   53.9ms
    +  6   -7.905211520448       -6.37       -4.68    1.0   58.3ms
    +  7   -7.905211531166       -7.97       -4.64    3.1   81.5ms
    +  8   -7.905211531386       -9.66       -5.18    1.0   61.1ms

    That's it! Now you can get various quantities from the result of the SCF. For instance, the different components of the energy:

    scfres.energies
    Energy breakdown (in Ha):
    +    Kinetic             3.1020960 
    +    AtomicLocal         -2.1987837
    +    AtomicNonlocal      1.7296091 
         Ewald               -8.3979253
         PspCorrection       -0.2946254
    -    Hartree             0.5530369 
    -    Xc                  -2.3986204
    +    Hartree             0.5530389 
    +    Xc                  -2.3986211
     
    -    total               -7.905211531367

    Eigenvalues:

    hcat(scfres.eigenvalues...)
    7×8 Matrix{Float64}:
    - -0.176941  -0.14744   -0.0911685   …  -0.101218  -0.0239763  -0.0184072
    -  0.261074   0.116915   0.00482558      0.061165  -0.0239763  -0.0184072
    -  0.261074   0.23299    0.216734        0.121636   0.155532    0.117748
    -  0.261074   0.23299    0.216734        0.212135   0.155532    0.117748
    -  0.354533   0.33511    0.317103        0.350436   0.285692    0.417258
    -  0.354533   0.389829   0.384601    …   0.436926   0.285692    0.417429
    -  0.354533   0.389829   0.384601        0.449243   0.62754     0.443807

    eigenvalues is an array (indexed by k-points) of arrays (indexed by eigenvalue number). The "splatting" operation ... calls hcat with all the inner arrays as arguments, which collects them into a matrix.

    The resulting matrix is 7 (number of computed eigenvalues) by 8 (number of irreducible k-points). There are 7 eigenvalues per k-point because there are 4 occupied states in the system (4 valence electrons per silicon atom, two atoms per unit cell, and paired spins), and the eigensolver gives itself some breathing room by computing some extra states (see the bands argument to self_consistent_field as well as the AdaptiveBands documentation). There are only 8 k-points (instead of 4x4x4) because symmetry has been used to reduce the amount of computations to just the irreducible k-points (see Crystal symmetries for details).

    We can check the occupations ...

    hcat(scfres.occupation...)
    7×8 Matrix{Float64}:
    +    total               -7.905211531386

    Eigenvalues:

    hcat(scfres.eigenvalues...)
    7×8 Matrix{Float64}:
    + -0.176942  -0.14744   -0.0911691   …  -0.101219   -0.0239769  -0.0184079
    +  0.261073   0.116915   0.00482514      0.0611644  -0.0239769  -0.0184079
    +  0.261073   0.23299    0.216734        0.121636    0.155532    0.117747
    +  0.261073   0.23299    0.216734        0.212134    0.155532    0.117747
    +  0.354532   0.335109   0.317102        0.350436    0.285692    0.417258
    +  0.354532   0.389829   0.384601    …   0.436925    0.285692    0.417261
    +  0.354535   0.389829   0.384601        0.449232    0.627547    0.443806

    eigenvalues is an array (indexed by k-points) of arrays (indexed by eigenvalue number). The "splatting" operation ... calls hcat with all the inner arrays as arguments, which collects them into a matrix.

    The resulting matrix is 7 (number of computed eigenvalues) by 8 (number of irreducible k-points). There are 7 eigenvalues per k-point because there are 4 occupied states in the system (4 valence electrons per silicon atom, two atoms per unit cell, and paired spins), and the eigensolver gives itself some breathing room by computing some extra states (see the bands argument to self_consistent_field as well as the AdaptiveBands documentation). There are only 8 k-points (instead of 4x4x4) because symmetry has been used to reduce the amount of computations to just the irreducible k-points (see Crystal symmetries for details).

    We can check the occupations ...

    hcat(scfres.occupation...)
    7×8 Matrix{Float64}:
      2.0  2.0  2.0  2.0  2.0  2.0  2.0  2.0
      2.0  2.0  2.0  2.0  2.0  2.0  2.0  2.0
      2.0  2.0  2.0  2.0  2.0  2.0  2.0  2.0
    @@ -59,6 +59,6 @@
      0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
      0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0

    ... and density, where we use that the density objects in DFTK are indexed as ρ[iσ, ix, iy, iz], i.e. first in the spin component and then in the 3-dimensional real-space grid.

    rvecs = collect(r_vectors(basis))[:, 1, 1]  # slice along the x axis
     x = [r[1] for r in rvecs]                   # only keep the x coordinate
    -plot(x, scfres.ρ[1, :, 1, 1], label="", xlabel="x", ylabel="ρ", marker=2)
    Example block output

    We can also perform various postprocessing steps: for instance compute a band structure

    plot_bandstructure(scfres; kline_density=10)
    Example block output

    or get the cartesian forces (in Hartree / Bohr)

    compute_forces_cart(scfres)
    2-element Vector{StaticArraysCore.SVector{3, Float64}}:
    - [-2.9876300399164333e-16, -5.794063929726965e-16, 2.3498993779195476e-17]
    - [-3.446409923064917e-16, 5.4799337373454367e-17, 5.642692929289822e-18]

    As expected, they are numerically zero in this highly symmetric configuration.

    +plot(x, scfres.ρ[1, :, 1, 1], label="", xlabel="x", ylabel="ρ", marker=2)Example block output

    We can also perform various postprocessing steps: for instance compute a band structure

    plot_bandstructure(scfres; kline_density=10)
    Example block output

    or get the cartesian forces (in Hartree / Bohr)

    compute_forces_cart(scfres)
    2-element Vector{StaticArraysCore.SVector{3, Float64}}:
    + [-7.747177043678906e-16, -1.8999262040369398e-16, -1.975106756082792e-17]
    + [1.313135438657421e-16, -4.2115519879877946e-16, -2.5400065726005693e-16]

    As expected, they are numerically zero in this highly symmetric configuration.

    diff --git a/dev/index.html b/dev/index.html index 87242c7c72..9958c4212d 100644 --- a/dev/index.html +++ b/dev/index.html @@ -1,2 +1,2 @@ -Home · DFTK.jl

    DFTK.jl: The density-functional toolkit.

    The density-functional toolkit, DFTK for short, is a library of Julia routines for playing with plane-wave density-functional theory (DFT) algorithms. In its basic formulation it solves periodic Kohn-Sham equations. The unique feature of the code is its emphasis on simplicity and flexibility with the goal of facilitating methodological development and interdisciplinary collaboration. In about 7k lines of pure Julia code we support a sizeable set of features. Our performance is of the same order of magnitude as much larger production codes such as Abinit, Quantum Espresso and VASP. DFTK's source code is publicly available on github.

    If you are new to density-functional theory or plane-wave methods, see our notes on Periodic problems and our collection of Introductory resources.

    Found a bug, missing a feature? Look for an open issue or create a new one. Want to contribute? See our contributing notes.

    Getting started

    First, new users should take a look at the Installation and Tutorial sections. Then, make your way through the various examples. An ideal starting point are the Examples on basic DFT calculations.

    Convergence parameters in the documentation

    In the documentation we use very rough convergence parameters to be able to automatically generate this documentation very quickly. Therefore results are far from converged. Tighter thresholds and larger grids should be used for more realistic results.

    If you have an idea for an addition to the docs or see something wrong, please open an issue or pull request!

    +Home · DFTK.jl

    DFTK.jl: The density-functional toolkit.

    The density-functional toolkit, DFTK for short, is a library of Julia routines for playing with plane-wave density-functional theory (DFT) algorithms. In its basic formulation it solves periodic Kohn-Sham equations. The unique feature of the code is its emphasis on simplicity and flexibility with the goal of facilitating methodological development and interdisciplinary collaboration. In about 7k lines of pure Julia code we support a sizeable set of features. Our performance is of the same order of magnitude as much larger production codes such as Abinit, Quantum Espresso and VASP. DFTK's source code is publicly available on github.

    If you are new to density-functional theory or plane-wave methods, see our notes on Periodic problems and our collection of Introductory resources.

    Found a bug, missing a feature? Look for an open issue or create a new one. Want to contribute? See our contributing notes.

    Getting started

    First, new users should take a look at the Installation and Tutorial sections. Then, make your way through the various examples. An ideal starting point are the Examples on basic DFT calculations.

    Convergence parameters in the documentation

    In the documentation we use very rough convergence parameters to be able to automatically generate this documentation very quickly. Therefore results are far from converged. Tighter thresholds and larger grids should be used for more realistic results.

    If you have an idea for an addition to the docs or see something wrong, please open an issue or pull request!

    diff --git a/dev/publications/index.html b/dev/publications/index.html index 5cc7d5a59a..29f7a91f3b 100644 --- a/dev/publications/index.html +++ b/dev/publications/index.html @@ -7,4 +7,4 @@ volume = {3}, pages = {69}, year = {2021}, -}

    Additionally the following publications describe DFTK or one of its algorithms:

    Research conducted with DFTK

    The following publications report research employing DFTK as a core component. Feel free to drop us a line if you want your work to be added here.

    +}

    Additionally the following publications describe DFTK or one of its algorithms:

    Research conducted with DFTK

    The following publications report research employing DFTK as a core component. Feel free to drop us a line if you want your work to be added here.

    diff --git a/dev/school2022/index.html b/dev/school2022/index.html index f42c0b2306..770805e1a4 100644 --- a/dev/school2022/index.html +++ b/dev/school2022/index.html @@ -1,2 +1,2 @@ -DFTK School 2022 · DFTK.jl
    +DFTK School 2022 · DFTK.jl
    diff --git a/dev/tricks/parallelization/index.html b/dev/tricks/parallelization/index.html index 19f9efe3ed..a3900cbbe9 100644 --- a/dev/tricks/parallelization/index.html +++ b/dev/tricks/parallelization/index.html @@ -5,34 +5,34 @@ DFTK.timer
     ────────────────────────────────────────────────────────────────────────────────
                                             Time                    Allocations      
                                    ───────────────────────   ────────────────────────
    -       Tot / % measured:            315ms /  50.2%           85.1MiB /  75.9%    
    +       Tot / % measured:            618ms /  52.2%           85.2MiB /  75.9%    
     
      Section               ncalls     time    %tot     avg     alloc    %tot      avg
      ────────────────────────────────────────────────────────────────────────────────
    - self_consistent_field      1    158ms   99.9%   158ms   64.6MiB  100.0%  64.6MiB
    -   LOBPCG                  27   70.0ms   44.3%  2.59ms   17.7MiB   27.4%   672KiB
    -     DftHamiltonian...     74   51.6ms   32.7%   697μs   5.95MiB    9.2%  82.4KiB
    -       local+kinetic      489   48.7ms   30.8%   100μs    382KiB    0.6%     800B
    -       nonlocal            74   1.09ms    0.7%  14.8μs   1.37MiB    2.1%  18.9KiB
    -     ortho! X vs Y         67   5.12ms    3.2%  76.5μs   1.99MiB    3.1%  30.4KiB
    -       ortho!             133   2.47ms    1.6%  18.5μs   1.03MiB    1.6%  7.95KiB
    -     rayleigh_ritz         47   4.78ms    3.0%   102μs   1.75MiB    2.7%  38.1KiB
    -       ortho!              47    596μs    0.4%  12.7μs    260KiB    0.4%  5.53KiB
    -     preconditioning       74   2.45ms    1.6%  33.1μs    348KiB    0.5%  4.70KiB
    -     Update residuals      74    921μs    0.6%  12.4μs   1.13MiB    1.8%  15.7KiB
    -     ortho!                27    584μs    0.4%  21.6μs    166KiB    0.3%  6.16KiB
    -   compute_density          9   54.7ms   34.6%  6.07ms   6.28MiB    9.7%   714KiB
    -     symmetrize_ρ           9   48.0ms   30.4%  5.34ms   5.01MiB    7.7%   570KiB
    -   energy_hamiltonian      19   27.2ms   17.2%  1.43ms   30.6MiB   47.4%  1.61MiB
    -     ene_ops               19   24.8ms   15.7%  1.30ms   20.0MiB   30.9%  1.05MiB
    -       ene_ops: xc         19   20.3ms   12.8%  1.07ms   8.93MiB   13.8%   481KiB
    -       ene_ops: har...     19   2.51ms    1.6%   132μs   8.57MiB   13.3%   462KiB
    -       ene_ops: non...     19    859μs    0.5%  45.2μs    297KiB    0.4%  15.6KiB
    -       ene_ops: kin...     19    427μs    0.3%  22.5μs    188KiB    0.3%  9.89KiB
    -       ene_ops: local      19    281μs    0.2%  14.8μs   1.74MiB    2.7%  94.0KiB
    -   ortho_qr                 3    138μs    0.1%  45.9μs    102KiB    0.2%  33.9KiB
    -   χ0Mixing                 9   63.8μs    0.0%  7.09μs   54.4KiB    0.1%  6.05KiB
    - enforce_real!              1   81.0μs    0.1%  81.0μs   1.69KiB    0.0%  1.69KiB
    + self_consistent_field      1    322ms  100.0%   322ms   64.6MiB  100.0%  64.6MiB
    +   LOBPCG                  27    154ms   47.6%  5.69ms   17.8MiB   27.5%   674KiB
    +     DftHamiltonian...     75    113ms   35.1%  1.51ms   6.00MiB    9.3%  82.0KiB
    +       local+kinetic      490    105ms   32.6%   214μs    383KiB    0.6%     800B
    +       nonlocal            75   3.00ms    0.9%  40.0μs   1.37MiB    2.1%  18.7KiB
    +     ortho! X vs Y         69   10.6ms    3.3%   154μs   1.97MiB    3.0%  29.2KiB
    +       ortho!             134   4.61ms    1.4%  34.4μs   1.02MiB    1.6%  7.81KiB
    +     rayleigh_ritz         48   9.24ms    2.9%   193μs   1.74MiB    2.7%  37.2KiB
    +       ortho!              48   1.27ms    0.4%  26.6μs    261KiB    0.4%  5.43KiB
    +     preconditioning       75   5.97ms    1.9%  79.6μs    349KiB    0.5%  4.65KiB
    +     Update residuals      75   2.37ms    0.7%  31.6μs   1.14MiB    1.8%  15.5KiB
    +     ortho!                27   1.13ms    0.4%  42.0μs    166KiB    0.3%  6.16KiB
    +   compute_density          9    102ms   31.6%  11.3ms   6.28MiB    9.7%   714KiB
    +     symmetrize_ρ           9   88.3ms   27.4%  9.81ms   5.01MiB    7.7%   570KiB
    +   energy_hamiltonian      19   54.7ms   17.0%  2.88ms   30.6MiB   47.3%  1.61MiB
    +     ene_ops               19   47.9ms   14.9%  2.52ms   20.0MiB   30.9%  1.05MiB
    +       ene_ops: xc         19   36.6ms   11.3%  1.93ms   8.93MiB   13.8%   481KiB
    +       ene_ops: har...     19   7.06ms    2.2%   371μs   8.57MiB   13.3%   462KiB
    +       ene_ops: non...     19   1.64ms    0.5%  86.1μs    297KiB    0.4%  15.6KiB
    +       ene_ops: local      19    864μs    0.3%  45.5μs   1.74MiB    2.7%  94.0KiB
    +       ene_ops: kin...     19    864μs    0.3%  45.5μs    188KiB    0.3%  9.89KiB
    +   ortho_qr                 3    266μs    0.1%  88.7μs    102KiB    0.2%  33.9KiB
    +   χ0Mixing                 9   94.4μs    0.0%  10.5μs   54.4KiB    0.1%  6.05KiB
    + enforce_real!              1    116μs    0.0%   116μs   1.69KiB    0.0%  1.69KiB
      ────────────────────────────────────────────────────────────────────────────────

    The output produced when printing or displaying the DFTK.timer now shows a nice table summarising total time and allocations as well as a breakdown over individual routines.

    Timing measurements and stack traces

    Timing measurements have the unfortunate disadvantage that they alter the way stack traces look making it sometimes harder to find errors when debugging. For this reason timing measurements can be disabled completely (i.e. not even compiled into the code) by setting the package-level preference DFTK.set_timer_enabled!(false). You will need to restart your Julia session afterwards to take this into account.

    Rough timing estimates

    A very (very) rough estimate of the time per SCF step (in seconds) can be obtained with the following function. The function assumes that FFTs are the limiting operation and that no parallelisation is employed.

    function estimate_time_per_scf_step(basis::PlaneWaveBasis)
         # Super rough figure from various tests on cluster, laptops, ... on a 128^3 FFT grid.
         time_per_FFT_per_grid_point = 30 #= ms =# / 1000 / 128^3
    @@ -49,4 +49,4 @@
     disable_threading()
  • Run Julia in parallel using the mpiexecjl wrapper script from MPI.jl:

    mpiexecjl -np 16 julia myscript.jl

    In this -np 16 tells MPI to use 16 processes and -t 1 tells Julia to use one thread only. Notice that we use mpiexecjl to automatically select the mpiexec compatible with the MPI version used by MPI.jl.

  • As usual with MPI printing will be garbled. You can use

    DFTK.mpi_master() || (redirect_stdout(); redirect_stderr())

    at the top of your script to disable printing on all processes but one.

    MPI-based parallelism not fully supported

    While standard procedures (such as the SCF or band structure calculations) fully support MPI, not all routines of DFTK are compatible with MPI yet and will throw an error when being called in an MPI-parallel run. In most cases there is no intrinsic limitation it just has not yet been implemented. If you require MPI in one of our routines, where this is not yet supported, feel free to open an issue on github or otherwise get in touch.

    Thread-based parallelism

    Threading in DFTK currently happens on multiple layers distributing the workload over different $k$-points, bands or within an FFT or BLAS call between threads. At its current stage our scaling for thread-based parallelism is worse compared MPI-based and therefore the parallelism described here should only be used if no other option exists. To use thread-based parallelism proceed as follows:

    1. Ensure that threading is properly setup inside DFTK by adding to the script running the DFTK calculation:

      using DFTK
       setup_threading()

      This disables FFT threading and sets the number of BLAS threads to the number of Julia threads.

    2. Run Julia passing the desired number of threads using the flag -t:

      julia -t 8 myscript.jl

    For some cases (e.g. a single $k$-point, fewish bands and a large FFT grid) it can be advantageous to add threading inside the FFTs as well. One example is the Caffeine calculation in the above scaling plot. In order to do so just call setup_threading(n_fft=2), which will select two FFT threads. More than two FFT threads is rarely useful.

    Advanced threading tweaks

    The default threading setup done by setup_threading is to select one FFT thread and the same number of BLAS and Julia threads. This section provides some info in case you want to change these defaults.

    BLAS threads

    All BLAS calls in Julia go through a parallelized OpenBlas or MKL (with MKL.jl. Generally threading in BLAS calls is far from optimal and the default settings can be pretty bad. For example for CPUs with hyper threading enabled, the default number of threads seems to equal the number of virtual cores. Still, BLAS calls typically take second place in terms of the share of runtime they make up (between 10% and 20%). Of note many of these do not take place on matrices of the size of the full FFT grid, but rather only in a subspace (e.g. orthogonalization, Rayleigh-Ritz, ...) such that parallelization is either anyway disabled by the BLAS library or not very effective. To set the number of BLAS threads use

    using LinearAlgebra
     BLAS.set_num_threads(N)

    where N is the number of threads you desire. To check the number of BLAS threads currently used, you can use

    Int(ccall((BLAS.@blasfunc(openblas_get_num_threads), BLAS.libblas), Cint, ()))

    or (from Julia 1.6) simply BLAS.get_num_threads().

    Julia threads

    On top of BLAS threading DFTK uses Julia threads (Thread.@threads) in a couple of places to parallelize over $k$-points (density computation) or bands (Hamiltonian application). The number of threads used for these aspects is controlled by the flag -t passed to Julia or the environment variable JULIA_NUM_THREADS. To check the number of Julia threads use Threads.nthreads().

    FFT threads

    Since FFT threading is only used in DFTK inside the regions already parallelized by Julia threads, setting FFT threads to something larger than 1 is rarely useful if a sensible number of Julia threads has been chosen. Still, to explicitly set the FFT threads use

    using FFTW
    -FFTW.set_num_threads(N)

    where N is the number of threads you desire. By default no FFT threads are used, which is almost always the best choice.

    +FFTW.set_num_threads(N)

    where N is the number of threads you desire. By default no FFT threads are used, which is almost always the best choice.

    diff --git a/dev/tricks/scf_checkpoints.ipynb b/dev/tricks/scf_checkpoints.ipynb index 9a8eb07582..5dc1768fd4 100644 --- a/dev/tricks/scf_checkpoints.ipynb +++ b/dev/tricks/scf_checkpoints.ipynb @@ -48,11 +48,12 @@ "text": [ "n Energy log10(ΔE) log10(Δρ) Magnet Diag Δtime\n", "--- --------------- --------- --------- ------ ---- ------\n", - " 1 -27.65172944926 -0.13 0.001 6.5 \n", - " 2 -28.92233774953 0.10 -0.82 0.672 2.0 83.7ms\n", - " 3 -28.93095481872 -2.06 -1.14 1.170 2.0 118ms\n", - " 4 -28.93760465579 -2.18 -1.18 1.763 1.0 88.1ms\n", - " 5 -28.93956970714 -2.71 -2.07 1.984 1.5 75.2ms\n" + " 1 -27.64538056326 -0.13 0.001 6.5 \n", + " 2 -28.92265173056 0.11 -0.82 0.676 2.0 237ms\n", + " 3 -28.93102318910 -2.08 -1.14 1.176 2.0 163ms\n", + " 4 -28.93763846736 -2.18 -1.18 1.766 1.0 136ms\n", + " 5 -28.93956441354 -2.72 -1.88 1.991 1.5 184ms\n", + " 6 -28.93960319425 -4.41 -2.26 1.982 1.0 140ms\n" ] } ], @@ -86,7 +87,7 @@ { "output_type": "execute_result", "data": { - "text/plain": "Energy breakdown (in Ha):\n Kinetic 16.7571342\n AtomicLocal -58.4652079\n AtomicNonlocal 4.7067657 \n Ewald -4.8994689\n PspCorrection 0.0044178 \n Hartree 19.3460843\n Xc -6.3883403\n Entropy -0.0009545\n\n total -28.939569707140" + "text/plain": "Energy breakdown (in Ha):\n Kinetic 16.7686394\n AtomicLocal -58.4922640\n AtomicNonlocal 4.7114654 \n Ewald -4.8994689\n PspCorrection 0.0044178 \n Hartree 19.3591916\n Xc -6.3905996\n Entropy -0.0009849\n\n total -28.939603194249" }, "metadata": {}, "execution_count": 2 @@ -134,7 +135,7 @@ { "output_type": "execute_result", "data": { - "text/plain": "Energy breakdown (in Ha):\n Kinetic 16.7571342\n AtomicLocal -58.4652079\n AtomicNonlocal 4.7067657 \n Ewald -4.8994689\n PspCorrection 0.0044178 \n Hartree 19.3460843\n Xc -6.3883403\n Entropy -0.0009545\n\n total -28.939569707140" + "text/plain": "Energy breakdown (in Ha):\n Kinetic 16.7686394\n AtomicLocal -58.4922640\n AtomicNonlocal 4.7114654 \n Ewald -4.8994689\n PspCorrection 0.0044178 \n Hartree 19.3591916\n Xc -6.3905996\n Entropy -0.0009849\n\n total -28.939603194249" }, "metadata": {}, "execution_count": 4 @@ -202,13 +203,13 @@ "text": [ "n Energy log10(ΔE) log10(Δρ) Magnet α Diag Δtime\n", "--- --------------- --------- --------- ------ ---- ---- ------\n", - " 1 -27.64741440815 -0.13 0.001 0.80 6.0 \n", - " 2 -28.92246903345 0.11 -0.82 0.659 0.80 2.0 97.8ms\n", - " 3 -28.93079067031 -2.08 -1.15 1.153 0.80 2.0 158ms\n", - " 4 -28.93746834673 -2.18 -1.18 1.750 0.80 1.0 76.3ms\n", - " 5 -28.93936699586 -2.72 -1.45 1.940 0.80 1.0 76.5ms\n", - " 6 -28.93958871449 -3.65 -1.94 1.991 0.80 1.5 105ms\n", - " 7 -28.93960856050 -4.70 -2.33 1.982 0.80 1.0 78.3ms\n" + " 1 -27.64894258488 -0.13 0.001 0.80 6.0 \n", + " 2 -28.92235464064 0.10 -0.83 0.655 0.80 2.0 169ms\n", + " 3 -28.93074286809 -2.08 -1.15 1.150 0.80 2.5 183ms\n", + " 4 -28.93744512657 -2.17 -1.18 1.748 0.80 1.0 204ms\n", + " 5 -28.93939612521 -2.71 -1.47 1.944 0.80 1.0 149ms\n", + " 6 -28.93958607519 -3.72 -1.83 1.993 0.80 1.5 152ms\n", + " 7 -28.93960779008 -4.66 -2.17 1.981 0.80 1.0 197ms\n" ] } ], @@ -250,7 +251,7 @@ "text": [ "n Energy log10(ΔE) log10(Δρ) Magnet Diag Δtime\n", "--- --------------- --------- --------- ------ ---- ------\n", - " 1 -28.93961216868 -3.19 1.985 1.0 \n" + " 1 -28.93961243893 -3.12 1.985 1.0 \n" ] } ], diff --git a/dev/tricks/scf_checkpoints/index.html b/dev/tricks/scf_checkpoints/index.html index 0aa94e6076..54166b4d5a 100644 --- a/dev/tricks/scf_checkpoints/index.html +++ b/dev/tricks/scf_checkpoints/index.html @@ -19,36 +19,35 @@ scfres = self_consistent_field(basis, tol=1e-2, ρ=guess_density(basis, magnetic_moments)) save_scfres("scfres.jld2", scfres);
    n     Energy            log10(ΔE)   log10(Δρ)   Magnet   Diag   Δtime
     ---   ---------------   ---------   ---------   ------   ----   ------
    -  1   -27.65289669023                   -0.13    0.001    6.5
    -  2   -28.92271603943        0.10       -0.82    0.674    2.0   74.2ms
    -  3   -28.93097065305       -2.08       -1.14    1.174    2.0   74.7ms
    -  4   -28.93765158344       -2.18       -1.18    1.767    1.0   64.9ms
    -  5   -28.93951485756       -2.73       -1.44    1.998    1.5   75.4ms
    -  6   -28.93959610909       -4.09       -1.98    1.977    1.0   98.4ms
    -  7   -28.93961121956       -4.82       -3.11    1.985    1.0   92.3ms
    scfres.energies
    Energy breakdown (in Ha):
    -    Kinetic             16.7717789
    -    AtomicLocal         -58.4958952
    -    AtomicNonlocal      4.7102299 
    +  1   -27.64509695276                   -0.13    0.001    6.0
    +  2   -28.92282345683        0.11       -0.82    0.668    2.0    179ms
    +  3   -28.93088478780       -2.09       -1.14    1.165    2.0    176ms
    +  4   -28.93758706610       -2.17       -1.18    1.761    1.0    242ms
    +  5   -28.93955727402       -2.71       -1.79    1.993    1.5    159ms
    +  6   -28.93960019248       -4.37       -2.10    1.980    1.0    149ms
    scfres.energies
    Energy breakdown (in Ha):
    +    Kinetic             16.7688502
    +    AtomicLocal         -58.4928337
    +    AtomicNonlocal      4.7114614 
         Ewald               -4.8994689
         PspCorrection       0.0044178 
    -    Hartree             19.3614272
    -    Xc                  -6.3912449
    -    Entropy             -0.0008561
    +    Hartree             19.3596063
    +    Xc                  -6.3905853
    +    Entropy             -0.0010480
     
    -    total               -28.939611219559

    The scfres.jld2 file could now be transfered to a different computer, Where one could fire up a REPL to inspect the results of the above calculation:

    using DFTK
    +    total               -28.939600192484

    The scfres.jld2 file could now be transfered to a different computer, Where one could fire up a REPL to inspect the results of the above calculation:

    using DFTK
     using JLD2
     loaded = load_scfres("scfres.jld2")
     propertynames(loaded)
    (:ham, :basis, :energies, :converged, :occupation_threshold, :ρ, :α, :eigenvalues, :occupation, :εF, :n_bands_converge, :n_iter, :ψ, :diagonalization, :stage, :algorithm, :norm_Δρ)
    loaded.energies
    Energy breakdown (in Ha):
    -    Kinetic             16.7717789
    -    AtomicLocal         -58.4958952
    -    AtomicNonlocal      4.7102299 
    +    Kinetic             16.7688502
    +    AtomicLocal         -58.4928337
    +    AtomicNonlocal      4.7114614 
         Ewald               -4.8994689
         PspCorrection       0.0044178 
    -    Hartree             19.3614272
    -    Xc                  -6.3912449
    -    Entropy             -0.0008561
    +    Hartree             19.3596063
    +    Xc                  -6.3905853
    +    Entropy             -0.0010480
     
    -    total               -28.939611219559

    Since the loaded data contains exactly the same data as the scfres returned by the SCF calculation one could use it to plot a band structure, e.g. plot_bandstructure(load_scfres("scfres.jld2")) directly from the stored data.

    Checkpointing of SCF calculations

    A related feature, which is very useful especially for longer calculations with DFTK is automatic checkpointing, where the state of the SCF is periodically written to disk. The advantage is that in case the calculation errors or gets aborted due to overrunning the walltime limit one does not need to start from scratch, but can continue the calculation from the last checkpoint.

    To enable automatic checkpointing in DFTK one needs to pass the ScfSaveCheckpoints callback to self_consistent_field, for example:

    callback = DFTK.ScfSaveCheckpoints()
    +    total               -28.939600192484

    Since the loaded data contains exactly the same data as the scfres returned by the SCF calculation one could use it to plot a band structure, e.g. plot_bandstructure(load_scfres("scfres.jld2")) directly from the stored data.

    Checkpointing of SCF calculations

    A related feature, which is very useful especially for longer calculations with DFTK is automatic checkpointing, where the state of the SCF is periodically written to disk. The advantage is that in case the calculation errors or gets aborted due to overrunning the walltime limit one does not need to start from scratch, but can continue the calculation from the last checkpoint.

    To enable automatic checkpointing in DFTK one needs to pass the ScfSaveCheckpoints callback to self_consistent_field, for example:

    callback = DFTK.ScfSaveCheckpoints()
     scfres = self_consistent_field(basis;
                                    ρ=guess_density(basis, magnetic_moments),
                                    tol=1e-2, callback);

    Notice that using this callback makes the SCF go silent since the passed callback parameter overwrites the default value (namely DefaultScfCallback()) which exactly gives the familiar printing of the SCF convergence. If you want to have both (printing and checkpointing) you need to chain both callbacks:

    callback = DFTK.ScfDefaultCallback() ∘ DFTK.ScfSaveCheckpoints(keep=true)
    @@ -56,14 +55,14 @@
                                    ρ=guess_density(basis, magnetic_moments),
                                    tol=1e-2, callback);
    n     Energy            log10(ΔE)   log10(Δρ)   Magnet   α      Diag   Δtime
     ---   ---------------   ---------   ---------   ------   ----   ----   ------
    -  1   -27.64753980750                   -0.13    0.001   0.80    6.5
    -  2   -28.92266923130        0.11       -0.82    0.661   0.80    2.0   83.9ms
    -  3   -28.93084451579       -2.09       -1.14    1.160   0.80    2.0    147ms
    -  4   -28.93755956946       -2.17       -1.18    1.759   0.80    1.0   72.2ms
    -  5   -28.93956808131       -2.70       -2.08    1.984   0.80    1.5   76.7ms

    For more details on using callbacks with DFTK's self_consistent_field function see Monitoring self-consistent field calculations.

    By default checkpoint is saved in the file dftk_scf_checkpoint.jld2, which is deleted automatically once the SCF completes successfully. If one wants to keep the file one needs to specify keep=true as has been done in the ultimate SCF for demonstration purposes: now we can continue the previous calculation from the last checkpoint as if the SCF had been aborted. For this one just loads the checkpoint with load_scfres:

    oldstate = load_scfres("dftk_scf_checkpoint.jld2")
    +  1   -27.64790360864                   -0.13    0.001   0.80    6.5
    +  2   -28.92252201061        0.11       -0.82    0.675   0.80    2.0    246ms
    +  3   -28.93100518567       -2.07       -1.14    1.175   0.80    2.0    193ms
    +  4   -28.93762853003       -2.18       -1.18    1.765   0.80    1.0    160ms
    +  5   -28.93956589560       -2.71       -1.90    1.991   0.80    1.5    169ms
    +  6   -28.93960333822       -4.43       -2.27    1.982   0.80    1.5    166ms

    For more details on using callbacks with DFTK's self_consistent_field function see Monitoring self-consistent field calculations.

    By default checkpoint is saved in the file dftk_scf_checkpoint.jld2, which is deleted automatically once the SCF completes successfully. If one wants to keep the file one needs to specify keep=true as has been done in the ultimate SCF for demonstration purposes: now we can continue the previous calculation from the last checkpoint as if the SCF had been aborted. For this one just loads the checkpoint with load_scfres:

    oldstate = load_scfres("dftk_scf_checkpoint.jld2")
     scfres   = self_consistent_field(oldstate.basis, ρ=oldstate.ρ,
                                      ψ=oldstate.ψ, tol=1e-3);
    n     Energy            log10(ΔE)   log10(Δρ)   Magnet   Diag   Δtime
     ---   ---------------   ---------   ---------   ------   ----   ------
    -  1   -28.93960355372                   -2.79    1.985    1.0
    -  2   -28.93961040375       -5.16       -3.03    1.985    1.0   81.4ms
    Availability of `load_scfres`, `save_scfres` and `ScfSaveCheckpoints`

    As JLD2 is an optional dependency of DFTK these three functions are only available once one has both imported DFTK and JLD2 (using DFTK and using JLD2).

    (Cleanup files generated by this notebook)

    rm("dftk_scf_checkpoint.jld2")
    -rm("scfres.jld2")
    + 1 -28.93961089081 -3.01 1.985 1.0
    Availability of `load_scfres`, `save_scfres` and `ScfSaveCheckpoints`

    As JLD2 is an optional dependency of DFTK these three functions are only available once one has both imported DFTK and JLD2 (using DFTK and using JLD2).

    (Cleanup files generated by this notebook)

    rm("dftk_scf_checkpoint.jld2")
    +rm("scfres.jld2")