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

Aidan P. Thompson; H. Metin Aktulga; Richard Berger; Dan S. Bolintineanu; W. Michael Brown; Paul S. Crozier; Pieter J. in 't Veld; Axel Kohlmeyer; Stan G. Moore; Trung Dac Nguyen; Ray Shan; Mark J. Stevens; Julien Tranchida; Christian Trott; Steven J. Plimpton

doi:10.1016/j.cpc.2021.108171

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

Aidan P. Thompson^*, H. Metin Aktulga, Richard Berger, Dan S. Bolintineanu, W. Michael Brown, Paul S. Crozier, Pieter J. in 't Veld, Axel Kohlmeyer, Stan G. Moore, Trung Dac Nguyen, Ray Shan, Mark J. Stevens, Julien Tranchida, Christian Trott, Steven J. Plimpton

^*Corresponding author for this work

Chemical and Biological Engineering

Research output: Contribution to journal › Article › peer-review

5047 Scopus citations

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 Summary: 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. References: [1] S. Plimpton, Fast parallel algorithms for short-range molecular dynamics. J. Comp. Phys. 117 (1995) 1–19. [2] L. Verlet, Computer experiments on classical fluids: I. Thermodynamical properties of Lennard–Jones molecules, Phys. Rev. 159 (1967) 98–103.

Original language	English (US)
Article number	108171
Journal	Computer Physics Communications
Volume	271
DOIs	https://doi.org/10.1016/j.cpc.2021.108171
State	Published - Feb 2022

Funding

This work was supported in part by the Office of Fusion Energy Sciences program “Scientific Machine Learning and Artificial Intelligence.” Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government. This work was performed, in part, at the Center for Integrated Nanotechnologies, an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science. Much of the recent work on LAMMPS described in this paper was supported by the the EXAALT and CoPA projects within the Exascale Computing Project (No. 17-SC-20-SC) , a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration . We thank the many people who have contributed code and expertise to LAMMPS to help make it a broad and powerful tool. We have mentioned some of them in the text or footnotes or cited their papers, but unfortunately many other contributions could not be explicitly recognized, due to space limitations. See the Authors page https://www.lammps.org/authors.html on the LAMMPS website for a list of significant contributors. Much of the recent work on LAMMPS described in this paper was supported by the the EXAALT and CoPA projects within the Exascale Computing Project (No. 17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration. GPU benchmarking described in Section 5.1 was performed using the Lassen machine at Lawrence Livermore National Laboratory. The load-balancing results in Table 2 used resources of the Argonne Leadership Computing Facility, which is a U.S. Department of Energy Office of Science User Facility operated under contract DE-AC02-06CH11357. This work was performed, in part, at the Center for Integrated Nanotechnologies, an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science. This work was supported in part by the Office of Fusion Energy Sciences program ?Scientific Machine Learning and Artificial Intelligence.?, Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc. for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government.

Keywords

LAMMPS
Materials modeling
Molecular dynamics
Parallel algorithms

ASJC Scopus subject areas

Hardware and Architecture
General Physics and Astronomy

Access to Document

10.1016/j.cpc.2021.108171

Cite this

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. (2022). LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Computer Physics Communications, 271, Article 108171. https://doi.org/10.1016/j.cpc.2021.108171

@article{5aa1c0c4fd1d4a3a804df23f5e3cee1a,

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

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 Summary: 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 St{\o}rmer-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. References: [1] S. Plimpton, Fast parallel algorithms for short-range molecular dynamics. J. Comp. Phys. 117 (1995) 1–19. [2] L. Verlet, Computer experiments on classical fluids: I. Thermodynamical properties of Lennard–Jones molecules, Phys. Rev. 159 (1967) 98–103.",

keywords = "LAMMPS, Materials modeling, Molecular dynamics, Parallel algorithms",

author = "Thompson, {Aidan P.} and Aktulga, {H. Metin} and Richard Berger and Bolintineanu, {Dan S.} and Brown, {W. Michael} and Crozier, {Paul S.} and {in 't Veld}, {Pieter J.} and Axel Kohlmeyer and Moore, {Stan G.} and Nguyen, {Trung Dac} and Ray Shan and Stevens, {Mark J.} and Julien Tranchida and Christian Trott and Plimpton, {Steven J.}",

note = "Funding Information: This work was supported in part by the Office of Fusion Energy Sciences program “Scientific Machine Learning and Artificial Intelligence.” Funding Information: Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government. Funding Information: This work was performed, in part, at the Center for Integrated Nanotechnologies, an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science. Funding Information: Much of the recent work on LAMMPS described in this paper was supported by the the EXAALT and CoPA projects within the Exascale Computing Project (No. 17-SC-20-SC) , a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration . Funding Information: We thank the many people who have contributed code and expertise to LAMMPS to help make it a broad and powerful tool. We have mentioned some of them in the text or footnotes or cited their papers, but unfortunately many other contributions could not be explicitly recognized, due to space limitations. See the Authors page https://www.lammps.org/authors.html on the LAMMPS website for a list of significant contributors. Much of the recent work on LAMMPS described in this paper was supported by the the EXAALT and CoPA projects within the Exascale Computing Project (No. 17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration. GPU benchmarking described in Section 5.1 was performed using the Lassen machine at Lawrence Livermore National Laboratory. The load-balancing results in Table 2 used resources of the Argonne Leadership Computing Facility, which is a U.S. Department of Energy Office of Science User Facility operated under contract DE-AC02-06CH11357. This work was performed, in part, at the Center for Integrated Nanotechnologies, an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science. This work was supported in part by the Office of Fusion Energy Sciences program ?Scientific Machine Learning and Artificial Intelligence.?, Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc. for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government. Publisher Copyright: {\textcopyright} 2021 The Author(s)",

year = "2022",

month = feb,

doi = "10.1016/j.cpc.2021.108171",

language = "English (US)",

volume = "271",

journal = "Computer Physics Communications",

issn = "0010-4655",

publisher = "Elsevier B.V.",

}

Thompson, AP, Aktulga, HM, Berger, R, Bolintineanu, DS, Brown, WM, Crozier, PS, in 't Veld, PJ, Kohlmeyer, A, Moore, SG, Nguyen, TD, Shan, R, Stevens, MJ, Tranchida, J, Trott, C & Plimpton, SJ 2022, 'LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales', Computer Physics Communications, vol. 271, 108171. https://doi.org/10.1016/j.cpc.2021.108171

TY - JOUR

T1 - LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales

AU - Thompson, Aidan P.

AU - Aktulga, H. Metin

AU - Berger, Richard

AU - Bolintineanu, Dan S.

AU - Brown, W. Michael

AU - Crozier, Paul S.

AU - in 't Veld, Pieter J.

AU - Kohlmeyer, Axel

AU - Moore, Stan G.

AU - Nguyen, Trung Dac

AU - Shan, Ray

AU - Stevens, Mark J.

AU - Tranchida, Julien

AU - Trott, Christian

AU - Plimpton, Steven J.

N1 - Funding Information: This work was supported in part by the Office of Fusion Energy Sciences program “Scientific Machine Learning and Artificial Intelligence.” Funding Information: Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government. Funding Information: This work was performed, in part, at the Center for Integrated Nanotechnologies, an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science. Funding Information: Much of the recent work on LAMMPS described in this paper was supported by the the EXAALT and CoPA projects within the Exascale Computing Project (No. 17-SC-20-SC) , a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration . Funding Information: We thank the many people who have contributed code and expertise to LAMMPS to help make it a broad and powerful tool. We have mentioned some of them in the text or footnotes or cited their papers, but unfortunately many other contributions could not be explicitly recognized, due to space limitations. See the Authors page https://www.lammps.org/authors.html on the LAMMPS website for a list of significant contributors. Much of the recent work on LAMMPS described in this paper was supported by the the EXAALT and CoPA projects within the Exascale Computing Project (No. 17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration. GPU benchmarking described in Section 5.1 was performed using the Lassen machine at Lawrence Livermore National Laboratory. The load-balancing results in Table 2 used resources of the Argonne Leadership Computing Facility, which is a U.S. Department of Energy Office of Science User Facility operated under contract DE-AC02-06CH11357. This work was performed, in part, at the Center for Integrated Nanotechnologies, an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science. This work was supported in part by the Office of Fusion Energy Sciences program ?Scientific Machine Learning and Artificial Intelligence.?, Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc. for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government. Publisher Copyright: © 2021 The Author(s)

PY - 2022/2

Y1 - 2022/2

N2 - 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 Summary: 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. References: [1] S. Plimpton, Fast parallel algorithms for short-range molecular dynamics. J. Comp. Phys. 117 (1995) 1–19. [2] L. Verlet, Computer experiments on classical fluids: I. Thermodynamical properties of Lennard–Jones molecules, Phys. Rev. 159 (1967) 98–103.

AB - 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 Summary: 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. References: [1] S. Plimpton, Fast parallel algorithms for short-range molecular dynamics. J. Comp. Phys. 117 (1995) 1–19. [2] L. Verlet, Computer experiments on classical fluids: I. Thermodynamical properties of Lennard–Jones molecules, Phys. Rev. 159 (1967) 98–103.

KW - LAMMPS

KW - Materials modeling

KW - Molecular dynamics

KW - Parallel algorithms

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

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