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Cited by 12,598Parallel Computing and Optimization TechniquesAdvanced Physical and Chemical Molecular InteractionsComputer Graphics and Visualization Techniques

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LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales

Open access
Thompson, Aidan P., Aktulga, H. Metin, Berger, Richard, Bolintineanu, Dan S., Brown, W. Michael, Crozier, Paul S., in 't Veld, Pieter J., Kohlmeyer, Axel, Moore, Stan G., Nguyen, Trung Dac, Shan, Ray, Stevens, Mark J., Tranchida, Julien, Trott, Christian, Plimpton, Steven J.·2022·Computer Physics Communications
DOI 10.1016/j.cpc.2021.108171
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Abstract

Since the classical molecular dynamics simulator LAMMPS was released as an open source code in 2004, it has become a widely-used tool for particle-based modeling of materials at length scales ranging from atomic to mesoscale to continuum. Reasons for its popularity are that it provides a wide variety of particle interaction models for different materials, that it runs on any platform from a single CPU core to the largest supercomputers with accelerators, and that it gives users control over simulation details, either via the input script or by adding code for new interatomic potentials, constraints, diagnostics, or other features needed for their models. As a result, hundreds of people have contributed new capabilities to LAMMPS and it has grown from fifty thousand lines of code in 2004 to a million lines today. In this paper several of the fundamental algorithms used in LAMMPS are described along with the design strategies which have made it flexible for both users and developers. We also highlight some capabilities recently added to the code which were enabled by this flexibility, including dynamic load balancing, on-the-fly visualization, magnetic spin dynamics models, and quantum-accuracy machine learning interatomic potentials. Program Title: Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) CPC Library link to program files: https://doi.org/10.17632/cxbxs9btsv.1 Developer's repository link: https://github.com/lammps/lammps Licensing provisions: GPLv2 Programming language: C++, Python, C, Fortran Supplementary material: https://www.lammps.org Nature of problem: Many science applications in physics, chemistry, materials science, and related fields require parallel, scalable, and efficient generation of long, stable classical particle dynamics trajectories. Within this common problem definition, there lies a great diversity of use cases, distinguished by different particle interaction models, external constraints, as well as timescales and lengthscales ranging from atomic to mesoscale to macroscopic. Solution method: The LAMMPS code uses parallel spatial decomposition, distributed neighbor lists, and parallel FFTs for long-range Coulombic interactions [1]. The time integration algorithm is based on the Størmer-Verlet symplectic integrator [2], which provides better stability than higher-order non-symplectic methods. In addition, LAMMPS supports a wide range of interatomic potentials, constraints, diagnostics, software interfaces, and pre- and post-processing features. Additional comments including restrictions and unusual features: This paper serves as the definitive reference for the LAMMPS code. S. Plimpton, Fast parallel algorithms for short-range molecular dynamics. J. Comp. Phys. 117 (1995) 1–19. L. Verlet, Computer experiments on classical fluids: I. Thermodynamical properties of Lennard–Jones molecules, Phys. Rev. 159 (1967) 98–103.

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Research works printed but not linked: 38

  1. Yang, 2019, J. Chem. Phys., 151
  2. Anderson, 2002, Comput. Mater. Sci., 173
  3. Trott, 2021, IEEE Trans. Parallel Distrib. Syst., 1-1
  4. Moore, 2014, J. Chem. Phys., 140
  5. Derjaguin, 1941, Acta Physicochim. USSR, 14, 633
  6. Martinez, 2012, Phys. Rev. B, 86
  7. Larsen, 2017, J. Phys. Condens. Matter, 29
  8. Huang, 2018, Phys. Rev. Mater., 2
  9. Smith, 2020, Wiley Interdiscip. Rev.: Comput. Mol. Sci., 11
  10. Kondratyuk, 2021, Int. J. HPC Appl., 35, 312
  11. Stukowski, 2010, Model. Simul. Mater. Sci. Eng., 18
  12. Zhou, 2008, Phys. Rev. B, 78
  13. Jaramillo-Botero, 2011, J. Comput. Chem., 32, 497, 10.1002/jcc.21637 · DOI
  14. Kremer, 1990, J. Chem. Phys., 92, 5057, 10.1063/1.458541 · DOI · Paper page
  15. Ge, 2013, Phys. Rev. Lett., 110
  16. Shell, 2016, Adv. Chem. Phys., 161, 395
  17. Li, 2016, J. Chem. Phys., 145
  18. Noid, 2008, J. Chem. Phys., 128
  19. Noid, 2008, J. Chem. Phys., 128
  20. in 't Veld, 2008, Comput. Phys. Commun., 179, 320, 10.1016/j.cpc.2008.03.005 · DOI
  21. Kumar, 2010, Phys. Rev. E, 82
  22. Li, 2013, Phys. Fluids, 25
  23. Li, 2015, J. Chem. Phys., 143
  24. Santos, 2020, Phys. Rev. E
  25. Salerno, 2018, Phys. Rev. E, 98
  26. Wen, 2019, Phys. Rev. B, 100
  27. Mori, 2020, Phys. Rev. Mater., 4
  28. Daw, 1984, Phys. Rev. B, 29, 6443, 10.1103/PhysRevB.29.6443 · DOI
  29. Shan, 2010, Phys. Rev. B, 82
  30. Liang, 2013, Mater. Sci. Eng. R, 74, 255, 10.1016/j.mser.2013.07.001 · DOI
  31. Dragoni, 2018, Phys. Rev. Mater., 2
  32. Zhou, 2020, Phys. Rev. B, 101
  33. Voter, 1988, Phys. Rev. B, 57
  34. Plimpton, 2020, J. Chem. Phys.
  35. Kim, 2013, J. Chem. Phys., 139
  36. O'Connor, 2018, Phys. Rev. Lett., 121
  37. Reed, 2003, Phys. Rev. Lett., 90
  38. Tadmor, 2011, JOM, 63, 17, 10.1007/s11837-011-0102-6 · DOI

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Metadata

Authors
Thompson, A. P., Aktulga, H. M., Berger, R., Bolintineanu, D. S., Brown, W. M., Crozier, P. S., in 't Veld, P. J., Kohlmeyer, A., Moore, S. G., Nguyen, T. D., Shan, R., Stevens, M. J., Tranchida, J., Trott, C., & Plimpton, S. J.
Affiliations
  • Sandia National Laboratories (US)
  • Michigan State University (US)
  • Temple University (US)
  • Intel (United States) (US)
  • Northwestern University (US)
Corresponding author
  • Thompson, Aidan P.
  • Plimpton, Steven J.
ORCID
Venue
Computer Physics Communications
Publisher
Elsevier BV
Published
2022-02
Volume
271
Pages
108171
ISSN
0010-4655 (ISSN-L)
DOI
10.1016/j.cpc.2021.108171
OpenAlex
W3201073812
Cited by
12,598
References
240
Citations by year
2026: 3344 · 2025: 3748 · 2024: 2813 · 2023: 1870 · 2022: 807
FWCI
903.09
Type
journal article
Language
en
Open access
Licence
Locations
  • Computer Physics Communications (publishedVersion, cc-by-nc-nd)
  • TUScholarShare (Temple University) (submittedVersion, cc-by-nc-nd)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
Funders
  • Office of Science
  • National Nuclear Security Administration (DE-AC02-06CH11357)
  • Fusion Energy Sciences
  • Sandia National Laboratories (DE-NA0003525)
  • U.S. Department of Energy
Topics
  • Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
  • Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
  • Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computer Graphics and Visualization Techniques
Keywords
LAMMPS, molecular dynamics, interatomic potentials, parallel FFT, long-range Coulomb interaction, dynamic load balancing, machine learning interatomic potentials, open-source simulation software, multiscale materials modeling

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