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

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

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Thompson, Aidan P., Aktulga, H. Metin, Berger, Richard, Bolintineanu, Dan S., Brown, W. Michael, Crozier, Paul S., in 't Veld, Pieter J., Kohlmeyer, Axel +7 more·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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  • Researchers must typically integrate multiple tools such as Atomsk, LAMMPS, Phonopy, and OVITO, alongside literature or web search to retrieve force fields from databases like the NIST Interatomic Potentials Repository.

    “Researchers must typically integrate multiple tools such as, for example, Atomsk for structure generation, 7 LAMMPS for dynamics simulation, 8 Phonopy for simulation analysis, 9 and OVITO for visualization, 10 alongside literature or web search to retrieve force fields from relevant databases like the NIST Interatomic Potentials Repository.11”

    From Multi-agentic AI framework for end-to-end atomistic simulations (2026) · cites this paper as [8]

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  1. M. Griebel, S. Knapek, G. Zumbusch, Numerical Simulation in Molecular Dy- namics, Springer Verlag, Heidelberg Germany, 2007.
  2. C. Trott, D. Lebrun-Grandie, D. Arndt, J. Ciesko, V. Dang, N. Ellingwood, R. Gay- atri, E. Harvey, D.S. Hollman, D.A. Ibanez, N. Liber, J. Madsen, J.S. Miles, D.Z. Poliakoff, A.J. Powell, S. Rajamanickam, M. Simberg, D. Sunderland, B. Tur- cksin, J. Wilke, IEEE Trans. Parallel Distrib. Syst. (2021) 1-1.
  3. G. Sutmann, in: A. Bajaj, P. Zavattieri, M. Koslowski, T. Siegmund (Eds.), Pro- ceedings of the Society of Engineering Science 51st Annual Technical Meeting, Purdue University Libraries Scholarly Publishing Services, 2014.
  4. S.J. Plimpton, R. Pollock, M. Stevens, in: Proceedings of the Eighth SIAM Con- ference on Parallel Processing for Scientific Computing, 1997.
  5. M. Frigo, S.G. Johnson, Proc. IEEE 93 (2) (2005) 216-231; FFTW website: http://www.fftw.org, 2021.
  6. D. Smith, D. Altarawy, L. Burns, M. Welborn, L. Naden, L. Ward, S. Ellis, T. Crawford, Wiley Interdiscip. Rev.: Comput. Mol. Sci. 11 (2020) e1491.
  7. M.J. Berger, S.H. Bokhari, IEEE Trans. Comput. C-36 (5) (1987) 570-580.
  8. P.-W. Ma, S. Dudarev, in: Handbook of Materials Modeling: Methods: Theory and Modeling, 2020, pp. 1017-1035.
  9. R.F. Evans, in: Handbook of Materials Modeling: Applications: Current and Emerging Materials, 2020, pp. 427-448.
  10. J.A. Rackers, R.R. Silva, Z. Wang, J.W. Ponder, arXiv preprint arXiv:2106 .13116, 2021.

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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 · 2021: 16
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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