Biased Sampling Methods
MetaQCD.BiasModule.calc_weights — MethodWeighting schemes based on the ones compared in
https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.9b00867
MetaQCD.BiasModule.Bias — TypeBias(p::ParameterSet, U::Gaugefield; instance=1)Container that holds general parameters of bias enhanced sampling, like the kind of CV, its smearing and filenames/-pointers relevant to the bias. Also holds the specific kind of bias (Metadynamics, OPES or Parametric for now).
The instance keyword is used in case of PT-MetaD and multiple walkers to assign the correct usebias to each stream.
MetaQCD.BiasModule.Metadynamics — TypeMetadynamics(; symmetric=true, stride=1, cvlims=(-6, 6), biasfactor=Inf,
+Biased Sampling Methods · MetaQCD.jl Biased Sampling Methods
MetaQCD.BiasModule.calc_weights — MethodWeighting schemes based on the ones compared in
https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.9b00867
sourceMetaQCD.BiasModule.Bias — TypeBias(p::ParameterSet, U::Gaugefield; instance=1)
Container that holds general parameters of bias enhanced sampling, like the kind of CV, its smearing and filenames/-pointers relevant to the bias. Also holds the specific kind of bias (Metadynamics, OPES or Parametric for now).
The instance keyword is used in case of PT-MetaD and multiple walkers to assign the correct usebias to each stream.
sourceMetaQCD.BiasModule.Metadynamics — TypeMetadynamics(; symmetric=true, stride=1, cvlims=(-6, 6), biasfactor=Inf,
bin_width=0.1, weight=0.01, penalty_weight=1000)
-Metadynamics(p::ParameterSet; instance=1)
Create an instance of a Metadynamics bias using the inputs or the parameters given in p.
Specifiable parameters
symmetric::Bool = true - If true, the bias is built symmetrically by updating for both cv and -cv at every update-iteration
stride::Int64 = 1 - Number of iterations between updates; must be >0
cvlims::NTuple{2, Float64} = (-6, 6) - Minimum and maximum of the explorable cv-space; must be ordered
biasfactor::Float64 = Inf - Biasfactor for well-tempered Metadynamics; must be >1
bin_width::Float64 = 0.1 - Width of bins in histogram; must be >0
weight::Float64 = 0.01 - (Starting) Height of added Gaussians; must be positive
penalty_weight::Float64 = 1000 - Penalty when cv is outside of cvlims; must be positive
sourceMetaQCD.BiasModule.OPES — TypeOPES(; symmetric=true, stride=1, cvlims=(-6, 6), barrier=30,
+Metadynamics(p::ParameterSet; instance=1)
Create an instance of a Metadynamics bias using the inputs or the parameters given in p.
Specifiable parameters
symmetric::Bool = true - If true, the bias is built symmetrically by updating for both cv and -cv at every update-iteration
stride::Int64 = 1 - Number of iterations between updates; must be >0
cvlims::NTuple{2, Float64} = (-6, 6) - Minimum and maximum of the explorable cv-space; must be ordered
biasfactor::Float64 = Inf - Biasfactor for well-tempered Metadynamics; must be >1
bin_width::Float64 = 0.1 - Width of bins in histogram; must be >0
weight::Float64 = 0.01 - (Starting) Height of added Gaussians; must be positive
penalty_weight::Float64 = 1000 - Penalty when cv is outside of cvlims; must be positive
sourceMetaQCD.BiasModule.OPES — TypeOPES(; symmetric=true, stride=1, cvlims=(-6, 6), barrier=30,
biasfactor=Inf, σ₀=0.1, σ_min=1e-6, fixed_σ=true, opes_epsilon=0.0,
no_Z=false, threshold=1.0, cutoff=0.0)
-OPES(p::ParameterSet; instance=1)
Create an instance of a OPES bias using the parameters given in p.
Specifiable parameters
symmetric::Bool = true - If true, the bias is built symmetrically by updating for both cv and -cv at every update-iteration
stride::Int64 = 1 - Number of iterations between updates; must be >0
cvlims::NTuple{2, Float64} = (-6, 6) - Minimum and maximum of the explorable cv-space; must be ordered
barrier::Float64 = 30 - Estimate of height of action barriers
biasfactor::Float64 = Inf - Biasfactor for well-tempered OPES; must be >1
σ₀::Float64 = 0.1 - (Starting) width of kernels; must be >0
σ_min::Float64 = 1e-6 - Minimum width of kernels; must be >0
fixed_σ::Bool = true - If true, width if kernels decreases iteratively
ϵ::Float64 = exp(-barrier/(1-1/biasfactor)) - Determines maximum height of bias; must be >0
no_Z::Bool = false - If false normalization factor Z is dynamically adjusted
threshold::Float64 = 1.0 - Threshold distance for kernel merging; must be >0
cutoff::Float64 = sqrt(2barrier/(1-1/biasfactor)) - Cutoff value for kernels; must be >0
penalty::Float64 = exp(-0.5cutoff²) - Penalty for being outside kernel cutoff; must be >0
sourceMetaQCD.BiasModule.Parametric — TypeParametric(cvlims, penalty_weight, Q, A, Z)
-Parametric(p::ParameterSet; instance=1)
Create an instance of a static Parametric bias using the inputs or the parameters given in p.
Specifiable parameters
cvlims::NTuple{2, Float64} = (-6, 6) - Minimum and maximum of the explorable cv-space; must be ordered
penalty_weight::Float64 = 1000 - Penalty when cv is outside of cvlims; must be positive
Q::Float64 = 0 - Quadratic term in the bias
A::Float64 = 0 - Amplitude of the cosine term in the bias
Z::Float64 = 0 - Frequency of the cosine term in the bias
sourceSettings
This document was generated with Documenter.jl version 1.3.0 on Monday 12 August 2024. Using Julia version 1.9.4.
Create an instance of a OPES bias using the parameters given in p.
Specifiable parameters
symmetric::Bool = true - If true, the bias is built symmetrically by updating for both cv and -cv at every update-iteration stride::Int64 = 1 - Number of iterations between updates; must be >0 cvlims::NTuple{2, Float64} = (-6, 6) - Minimum and maximum of the explorable cv-space; must be ordered barrier::Float64 = 30 - Estimate of height of action barriers biasfactor::Float64 = Inf - Biasfactor for well-tempered OPES; must be >1 σ₀::Float64 = 0.1 - (Starting) width of kernels; must be >0 σ_min::Float64 = 1e-6 - Minimum width of kernels; must be >0 fixed_σ::Bool = true - If true, width if kernels decreases iteratively ϵ::Float64 = exp(-barrier/(1-1/biasfactor)) - Determines maximum height of bias; must be >0 no_Z::Bool = false - If false normalization factor Z is dynamically adjusted threshold::Float64 = 1.0 - Threshold distance for kernel merging; must be >0 cutoff::Float64 = sqrt(2barrier/(1-1/biasfactor)) - Cutoff value for kernels; must be >0 penalty::Float64 = exp(-0.5cutoff²) - Penalty for being outside kernel cutoff; must be >0
MetaQCD.BiasModule.Parametric — TypeParametric(cvlims, penalty_weight, Q, A, Z)
+Parametric(p::ParameterSet; instance=1)Create an instance of a static Parametric bias using the inputs or the parameters given in p.
Specifiable parameters
cvlims::NTuple{2, Float64} = (-6, 6) - Minimum and maximum of the explorable cv-space; must be ordered penalty_weight::Float64 = 1000 - Penalty when cv is outside of cvlims; must be positive Q::Float64 = 0 - Quadratic term in the bias A::Float64 = 0 - Amplitude of the cosine term in the bias Z::Float64 = 0 - Frequency of the cosine term in the bias