lammps_education_metal_win

• HEA = High Entropy Alloy

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lammps (windows 11 (64 bit))

Installation (Lammps)

1. LAMMPS Windows Installer Repository (http://packages.lammps.org/windows.html) -> LAMMPS Binaries Repository: ./legacy/admin/64bit (https://rpm.lammps.org/windows/legacy/admin/64bit/index.html)
2. LAMMPS-64bit-22Dec2022-MSMPI-admin.exe (https://rpm.lammps.org/windows/legacy/admin/64bit/LAMMPS-64bit-22Dec2022-MSMPI-admin.exe)

• LAMMPS Windows Installer Repository -> legacy -> admin -> 64bit -> LAMMPS-64bit-22Dec2022-MSMPI-admin.exe

Microsoft MPI

1. Microsoft MPI v10.1.2 (https://www.microsoft.com/en-us/download/details.aspx?id=100593)

Gnuplot, Ovito and Python3

• Gnuplot (http://www.gnuplot.info/) http://www.yamamo10.jp/yamamoto/comp/gnuplot/inst_win/index.php
• Ovito (https://www.ovito.org/windows-downloads/)

Python3 (installation) on PowerShell

1. python3
2. python3 -m pip install numpy

Usage (MS-MPI version)

1. click run_msmpi.bat
2. cfg folder > click *.cfg

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plot the temperature of each atom

• MSMPI_heat_map version file

1. *.cfg -> Ovito -> (upper right) Add modification...
2. Color coding -> Input property: f_ave_tempatom
3. (click) Adjust range

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lammps (windows 10 (64 bit))

Installation

1. LAMMPS Windows Installer Repository (http://packages.lammps.org/windows.html) > their own download area > 64bit (https://rpm.lammps.org/windows/admin/64bit/index.html)
2. LAMMPS-64bit-18Jun2019.exe (https://rpm.lammps.org/windows/admin/64bit/LAMMPS-64bit-18Jun2019.exe)

Gnuplot, Ovito and Python3

• Gnuplot (http://www.gnuplot.info/) http://www.yamamo10.jp/yamamoto/comp/gnuplot/inst_win/index.php
• Ovito (https://www.ovito.org/windows-downloads/)
• Python3 (Python 3.9, Python 3.8 or Python 3.7) from Microsoft Store

Usage

1. click run.bat
2. cfg folder > click *.cfg

How to change to another potential

1. notepad in.lmp

#----------(before)----------

#-----(Fe-Ni-Cr) (FCC)

#pair_style eam/fs

#pair_coeff * * ../potentials/eam/Fe-Ni-Cr_fcc.eam.fs Fe Ni Cr

#-----(Fe-Cr-W)

pair_style hybrid/overlay eam/alloy eam/fs

pair_coeff * * eam/alloy ../potentials/eam/FeCrW_d.eam.alloy Fe Cr W

pair_coeff * * eam/fs ../potentials/eam/FeCrW_s.eam.fs Fe Cr W

#---------------------------

#----------(after)----------

#-----(Fe-Ni-Cr) (FCC)

pair_style eam/fs

pair_coeff * * ../potentials/eam/Fe-Ni-Cr_fcc.eam.fs Fe Ni Cr

#-----(Fe-Cr-W)

#pair_style hybrid/overlay eam/alloy eam/fs

#pair_coeff * * eam/alloy ../potentials/eam/FeCrW_d.eam.alloy Fe Cr W

#pair_coeff * * eam/fs ../potentials/eam/FeCrW_s.eam.fs Fe Cr W

#---------------------------

Reference_eam_database (Generalized EAM = GEAM)

• official version: Mg, Ti, Zr, Ta, Mo, W, Fe, Co, Ni, Pd, Pt, Cu, Ag, Au, Al, Pb
• test version: (good <=) V, Ca, Na, Cr, Mn, Nb, Ir, Sr, Rh, Ru, Os, Hf, Re, Zn (=> bad) (compared to QE (DFT+PAW))
• Other elements have parameters, but don't expect accuracy. (e.g., Be, Y, Sn, B, etc)
• EAM does not consider angular dependence. Keep in mind that a system with strong angular dependence will have poor prediction accuracy.
• Any combination of these is possible. Don't expect too much about the "test version".

1. (open) Reference_eam_database
2. (open) EAM.input (on notepad, etc)
3. (Rewrite the elements, or reduce or increase the set of "&funccard" to "&end".)
4. (click) run.bat
5. (You can get EAM potential: XX_Zhou04.eam.alloy)

tutorial_8_elastic

1. notepad potential.mod
2. run.bat

• notepad potential.mod (set potential)
• notepad init.mod (set masses for sw, tersoff or bop potential)

tutorial_9_elastic_Temp

1. notepad potential.mod
2. notepad init.mod
3. run.bat

• notepad potential.mod (set potential)
• notepad init.mod (set temperature and masses)

tutorial_10_phonon-primitive-cell

• "Alamode+Lammps" (https://github.com/by-student-2017/alamode-example.git) may be better for phonon calculation.
• "Alamode" is designed to be able to calculate finite temperatures efficiently, so it is a code worthy of careful consideration. The interface is more suitable for intermediate and advanced users than Phonopy, but I also like the fact that it officially supports Lammps.
• If you really want to calculate the phonons, you should consider the method of calculating 0 K with "Alamode + DFTB+ & xTB potential" (https://github.com/by-student-2017/alamode-example/tree/main/Si_DFTBplus).

tutorial_11_hybrid

• The Example of EAM + MEAM + ADP hybrid potential
• If you are lucky enough to have all the potentials you want to calculate, even if they are separate, you can calculate them by hybridizing them like this.

tutorial_12_interface

• It is an example of an interface. In addition, I recommend watching “Tutorials_by_NextZenStudent” and Youtube videos.

tutorial_13_Machine_Learning_potential_MSMPI

• Calculation using potential by neural network. OpenKIM is available in the Linux version, so if you want to use more potential, please try the Linux version.

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Help create grain boundaries

• Build GB: https://aimsgb.org/ (Most recommend web page: Find GB -> Build GB (e.g., using Fe.cif from Material project))
• GB_code: https://github.com/oekosheri/GB_code
• GBstudio: home page disappeared

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units and potential

units metal

・Stillinger-Weber (SW)

・Tersoff

・EAM, FS

・MEAM

・ADP

・REBO, AIREBO

・COMB

・EIM

・BOP

・adiabatic core/shell model

・Streitz-Mintmire

・vashishta

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potential files

potentials

[1] NIST Interatomic Potential https://www.ctcms.nist.gov/potentials/ https://www.ctcms.nist.gov/potentials/resources.html

[2] Database of Published Interatomic Potential Parameters https://www.ucl.ac.uk/klmc/Potentials/

[3] EAM potentials https://sites.google.com/site/eampotentials/Home

[4] JARVIS for Force-fields https://www.ctcms.nist.gov/~knc6/periodic.html

[5] Embedded Atom Method (EAM) Tabulation https://atsimpotentials.readthedocs.io/en/latest/potentials/eam_tabulation.html

[6] Carbon Potentials http://www.carbonpotentials.org/potentials

[7] XMD - Molecular Dynamics for Metals and Ceramics http://xmd.sourceforge.net/eam.html

[8] Potentials generated with potfit https://www.potfit.net/wiki/doku.php?id=potentials:main

[9] Interatomic Potential Generation https://icme.hpc.msstate.edu/mediawiki/index.php/Interatomic_Potential_Generation

[10] Potentials https://norman.jiht.ru/wiki/index.php/Potentials

[11] Dr. Adri van Duin https://www.engr.psu.edu/adri/

[12] Welcome to the Knowledgebase of Interatomic Models! (OpenKIM) https://openkim.org/

[13] potential_LAMMPS Reference Records https://github.com/usnistgov/iprPy/tree/master/library/potential_LAMMPS

[14] KIST Integrated Force Field Platform http://kiff.vfab.org/

[15] Molecular Dynamics (MD) Simulations Based Design and Process Optimization of Solar Cells https://www.osti.gov/servlets/purl/1241668

[16] QC Method http://qcmethod.org/

[17] Buckingham database https://www5.hp-ez.com/hp/calculations/page515

Input file

Metal

[IFM1] P. Malakar et al., ACS Appl. Nano Mater. 5 (2022) 16489-16499. https://doi.org/10.1021/acsanm.2c03564 (lammps input file)

[IFM2] S. K. Achar et al., J. Chem. Theory Comput. 18 (2022) 3593-3606. https://doi.org/10.1021/acs.jctc.2c00010

[IFM3] M. Qamar et al., J. Chem. Theory, Comput. XXX (2023) XXX-XXXX. https://doi.org/10.1021/acs.jctc.2c01149

[IFM4] Y. A. Zulueta et al., Inorg. Chem. 59 (2020) 11841-11846. https://doi.org/10.1021/acs.inorgchem.0c01923 (Transition-Metal-Doped Li2SnO3)

[IFM5] M. Li et al., Nanomaterials 9 (2019) 347. https://doi.org/10.3390/nano9030347 (Graphene, The temperature of each atom)

[IFM6] Y.- P. Zhou et al., Sci. Rep. 7 (2017) 45516. https://www.nature.com/articles/srep45516

[IFM7] G. W. J. Mclntosh et al., (2016) https://cradpdf.drdc-rddc.gc.ca/PDFS/unc244/p804516_A1b.pdf

[IFM8] C. Wilkinson et al., SoftwareX 14 (2021) 100683. https://doi.org/10.1016/j.softx.2021.100683

[IFM9] Al-Cu Symmetric/Asymmetric Tilt Grain Boundary Dataset https://materialsdata.nist.gov/handle/11256/358

[IFM10] C.N. Andoh et al., Journal of Applied Science and Technology (JAST), Vol. 22, Nos. 1 & 2, 2017/18, pp. 01 - 13 https://www.researchgate.net/profile/Collins-Nana-Andoh/publication/327390429_MOLECULAR_DYNAMICS_SIMULATION_OF_MECHANICAL_DEFORMATION_OF_AUSTENITIC_STAINLESS_STEELS_Fe-Ni-Cr_ALLOYS_AT_SUPERCRITICAL_WATER_CONDITIONS/links/62ae336c938bee3e3f3f2253/MOLECULAR-DYNAMICS-SIMULATION-OF-MECHANICAL-DEFORMATION-OF-AUSTENITIC-STAINLESS-STEELS-Fe-Ni-Cr-ALLOYS-AT-SUPERCRITICAL-WATER-CONDITIONS.pdf

[IFM11] V. Kocevski et al., J. Nucl. Mater. 562 (2022) 153553. https://doi.org/10.1016/j.jnucmat.2022.153553

[IFM12] Z. Tang et al., Crystals 10 (2020) 329; https://doi.org/10.3390/cryst10040329

[IFM13] Z. Zhang, Thesis.; https://ttu-ir.tdl.org/handle/2346/73470

[IFM14] M. G. Muraleedharan et al., AIP Advances 7 (2017) 125022. https://doi.org/10.1063/1.5003158

[IFM15] A. S. Butterfield, https://www.byui.edu/documents/physics/Theses/2010-2015/Aaron-ButterfieldS13.pdf

Other

[IFO1] P. G. Boyd et al., J. Phys. Chem. Lett. 8 (2017) 357-363. https://doi.org/10.1021/acs.jpclett.6b02532 (MOF)

[IFO2] K. Banlusan et al., J. Phys. Chem. C 119 (2015) 25845-25852. https://doi.org/10.1021/acs.jpcc.5b05446 (MOF)

[IFO3] M. Witman et al., J. Phys. Chem. Lett. 10 (2019) 5929-5934. https://doi.org/10.1021/acs.jpclett.9b02449 (MOF)

[IFO4] J. P. Ruffley et al., J. Phys. Chem. C 124 (2020) 19873. https://doi.org/10.1021/acs.jpcc.0c07650 (MOF)

[IFO5] R. Anderson et al., Chem. Mater, 32 (2020) 8106-8119. https://doi.org/10.1021/acs.chemmater.0c00744 (MOF)

[IFO6] A. v. Wedelstedt et al., J. Chem. Inf. Model. 62 (2022) 1154-1159. https://doi.org/10.1021/acs.jcim.2c00158 (input file of MOF on Lammps and CP2k code)

[IFO7] J. J. Wardzala et al., J. Phys. Chem. C 124 (2020) 28469-28478. https://doi.org/10.1021/acs.jpcc.0c07040 (MOF)

[IFO8] M. C. Oliver et al., J. Phys. Chem. C 127 (2023) 6503-6514. https://doi.org/10.1021/acs.jpcc.2c08695 (MOF)

[IFO9] H. Xu et al., J. Chem. Theory Comput. 18 (2022) 2826-2835. https://doi.org/10.1021/acs.jctc.2c00094 (MOF) https://archive.materialscloud.org/record/2022.37

[IFO10] J. M. Findley et al., J. Phys. Chem. C 125 (2021) 8418-8429. https://doi.org/10.1021/acs.jpcc.1c00943 (input file of MOF on Lammps and RASPA code)

[IFO11] A. S. S. Daou et al., J. Phys. Chem. C 125 (2021) 5296-5305. https://doi.org/10.1021/acs.jpcc.0c09952 (input file of MOF on Lammps and RASPA code)

[IFO12] Z. Zhu et al., ACS Omega 7 (2022) 37640-37653. https://doi.org/10.1021/acsomega.2c04517 (input file of MOF on Lammps and RASPA code)

[IFO13] T. Weng et al., J. Phys. Chem. A 123 (2019) 3000-3012. https://doi.org/10.1021/acs.jpca.8b12311 (ZIF-8)

[IFO14] S. Wang et al., J. Chem. Theory Comput. 17 (2021) 5198-5213. https://doi.org/10.1021/acs.jctc.0c01132 (Zeolite)

[IFO15] P. Saidi et al., J. Phys. Chem. C 124 (2020) 26864-26873. https://doi.org/10.1021/acs.jpcc.0c08817 (GO)

[IFO16] M. L. Urquiza et al., ACS Nano 15 (2021) 12945-12954. https://doi.org/10.1021/acsnano.1c01466 (HfO2)

[IFO17] M. Deffner et al., J. Chem. Theory Comput. 19 (2023) 992-1002. https://doi.org/10.1021/acs.jctc.2c00648

[IFO18] W. A. Pisani et al., Ind. Eng. Chem. Res. 60 (2021) 13604-13613. https://doi.org/10.1021/acs.iecr.1c02440

[IFO19] K. Goloviznina et al., J. Chem. Theory Comput. 17 (2021) 1606-1617. https://doi.org/10.1021/acs.jctc.0c01002

[IFO20] C. Han et al., J. Phys. Chem. C 124 (2020) 20203-20212. https://doi.org/10.1021/acs.jpcc.0c05942

[IFO21] S. Sharma et al., J. Phys. Chem. A 124 (2020) 7832-7842. https://doi.org/10.1021/acs.jpca.0c06721

[IFO22] E. Braun et al., J. Chem. Theory Comput. 14 (2018) 5262-5272. https://doi.org/10.1021/acs.jctc.8b00446

[IFO23] Y. Chen et al., J. Phys. Chem. B 125 (2021) 8193-8204. https://doi.org/10.1021/acs.jpcb.1c01966

[IFO24] Y. Zhang et al., J. Phys. Chem. B 124 (2020) 5251-5264. https://doi.org/10.1021/acs.jpcb.0c04058

[IFO25] C. M. Tenney et al., J. Phys. Chem. C 117 (2013) 24673-24684. https://doi.org/10.1021/jp4039122

[IFO26] S. K. Achar et al., J. Phys. Chem. C 125 (2021) 14874-14882. https://doi.org/10.1021/acs.jpcc.1c01411

Structure

[S1] N. Sakhavand et al., ACS Appl. Mater. Interfaces 7 (2015) 18312-18319. https://doi.org/10.1021/acsami.5b03967

[S2] M. Agrawal et al., J. Phys. Chem. Lett. 10 (2019) 7823-7830. https://doi.org/10.1021/acs.jpclett.9b03119

[S3] R. Thyagarajan et al., Chem. Mater. 32 (2020) 8020-8033. https://doi.org/10.1021/acs.chemmater.0c03057

References

[MC1] Monte Carlo simulations with LAMMPS https://lammps.sandia.gov/workshops/Aug15/PDF/talk_Thompson1.pdf

[MC2] fix tfmc command (amorphous -> crystal) https://lammps.sandia.gov/doc/fix_tfmc.html https://lammps.sandia.gov/threads/msg69314.html https://lammps.sandia.gov/threads/msg69318.html https://lammps.sandia.gov/threads/msg69323.html https://lammps.sandia.gov/threads/msg69348.html https://lammps.sandia.gov/threads/msg69352.html

[SGC1] vcsgc-lammps (semi-grandcanonical) (pair_style eam/cd, eam/alloy or eam/fs) https://vcsgc-lammps.materialsmodeling.org/

[GCMC1] fix gcmc command https://lammps.sandia.gov/doc/fix_gcmc.html

[GCMC2] Grand canonical Monte Carlo simulations of gas uptake in microporous materials using LAMMPS https://www.osti.gov/servlets/purl/1120653

[GCMC3] pysimm https://www.sciencedirect.com/science/article/pii/S2352711018300141 (paper) https://pysimm.org/ (code)

[R1] Nanofluidics An Introduction https://books.google.co.jp/books?id=VwqWDwAAQBAJ

How to modify LAMMPS

http://pure-oai.bham.ac.uk/ws/portalfiles/portal/134808148/ChemEngineering_05_00030.pdf

Data-driven magneto-elastic predictions with scalable classical spin-lattice dynamics

Nikolov, S., et al., npj Comput Mater 7, 153 (2021). https://doi.org/10.1038/s41524-021-00617-2

Tersoff Benchmarking of Be-C-H Interatomic Potential

https://www.osti.gov/biblio/23047403

Deep-MD

Ren, Q., et al., Nat. Mater. (2023). https://doi.org/10.1038/s41563-023-01560-x