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

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 variet...

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Veröffentlicht in:	Computer physics communications Jg. 271; S. 108171
Hauptverfasser:	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.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.02.2022
Schlagworte:	LAMMPS Materials modeling Molecular dynamics Parallel algorithms LAMMPS Molecular dynamics Parallel algorithms Materials modeling
ISSN:	0010-4655, 1879-2944
Online-Zugang:	Volltext
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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. [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.
AbstractList	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. [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.
ArticleNumber	108171
Author	Moore, Stan G. Shan, Ray Aktulga, H. Metin Bolintineanu, Dan S. Nguyen, Trung Dac Thompson, Aidan P. Trott, Christian Crozier, Paul S. Plimpton, Steven J. Stevens, Mark J. Tranchida, Julien in 't Veld, Pieter J. Berger, Richard Brown, W. Michael Kohlmeyer, Axel
Author_xml	– sequence: 1 givenname: Aidan P. orcidid: 0000-0002-0324-9114 surname: Thompson fullname: Thompson, Aidan P. email: athomps@sandia.gov organization: Sandia National Laboratories, Albuquerque, NM 87185, USA – sequence: 2 givenname: H. Metin orcidid: 0000-0003-3061-6378 surname: Aktulga fullname: Aktulga, H. Metin organization: Michigan State University, East Lansing, MI 48824, USA – sequence: 3 givenname: Richard orcidid: 0000-0002-3044-8266 surname: Berger fullname: Berger, Richard organization: Temple University, Philadelphia, PA 19122, USA – sequence: 4 givenname: Dan S. surname: Bolintineanu fullname: Bolintineanu, Dan S. organization: Sandia National Laboratories, Albuquerque, NM 87185, USA – sequence: 5 givenname: W. Michael surname: Brown fullname: Brown, W. Michael organization: Intel Corporation, Hillsboro, OR 97124, USA – sequence: 6 givenname: Paul S. orcidid: 0000-0003-2966-6474 surname: Crozier fullname: Crozier, Paul S. organization: Sandia National Laboratories, Albuquerque, NM 87185, USA – sequence: 7 givenname: Pieter J. orcidid: 0000-0001-8460-5688 surname: in 't Veld fullname: in 't Veld, Pieter J. organization: BASF SE, Ludwigshafen am Rhein, Germany – sequence: 8 givenname: Axel orcidid: 0000-0001-6204-6475 surname: Kohlmeyer fullname: Kohlmeyer, Axel organization: Temple University, Philadelphia, PA 19122, USA – sequence: 9 givenname: Stan G. surname: Moore fullname: Moore, Stan G. organization: Sandia National Laboratories, Albuquerque, NM 87185, USA – sequence: 10 givenname: Trung Dac surname: Nguyen fullname: Nguyen, Trung Dac organization: Northwestern University, Evanston, IL 60208, USA – sequence: 11 givenname: Ray surname: Shan fullname: Shan, Ray organization: Materials Design Inc., San Diego, CA 92131, USA – sequence: 12 givenname: Mark J. surname: Stevens fullname: Stevens, Mark J. organization: Sandia National Laboratories, Albuquerque, NM 87185, USA – sequence: 13 givenname: Julien surname: Tranchida fullname: Tranchida, Julien organization: Sandia National Laboratories, Albuquerque, NM 87185, USA – sequence: 14 givenname: Christian surname: Trott fullname: Trott, Christian organization: Sandia National Laboratories, Albuquerque, NM 87185, USA – sequence: 15 givenname: Steven J. surname: Plimpton fullname: Plimpton, Steven J. email: sjplimp@sandia.gov organization: Sandia National Laboratories, Albuquerque, NM 87185, USA
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111 Phillips (10.1016/j.cpc.2021.108171_br0140) 2020; 153 Voth (10.1016/j.cpc.2021.108171_br1250) 2009 Mortier (10.1016/j.cpc.2021.108171_br1960) 1986; 108 Thompson (10.1016/j.cpc.2021.108171_br0430) 2009; 131 Pettifor (10.1016/j.cpc.2021.108171_br1040) 1999; 59 Parks (10.1016/j.cpc.2021.108171_br1590) 2008; 179 Kremer (10.1016/j.cpc.2021.108171_br1120) 1990; 92 Nguyen (10.1016/j.cpc.2021.108171_br2660) 2019; 241 Chauleau (10.1016/j.cpc.2021.108171_br2330) 2020; 19 Plimpton (10.1016/j.cpc.2021.108171_br0400) 2013; 17 Evans (10.1016/j.cpc.2021.108171_br2440) 1990 Barnes (10.1016/j.cpc.2021.108171_br0670) 2021; 261 Wen (10.1016/j.cpc.2021.108171_br1870) 2019; 100 Rackers (10.1016/j.cpc.2021.108171_bib739F96BADCFCA6FD03C1E5B17971C9DEs1) 2019; 150 Bollinger (10.1016/j.cpc.2021.108171_br0900) 2018; 14 Daw (10.1016/j.cpc.2021.108171_br1010) 1984; 29 Santos (10.1016/j.cpc.2021.108171_br1500) 2020 Todorov (10.1016/j.cpc.2021.108171_br0150) 2006; 16 Rutherford (10.1016/j.cpc.2021.108171_br0380) 2007; 20 Surh (10.1016/j.cpc.2021.108171_br2210) 2016; 117 Henrich (10.1016/j.cpc.2021.108171_br1190) 2018; 41 Stevens (10.1016/j.cpc.2021.108171_br1220) 2018; 149 Bock (10.1016/j.cpc.2021.108171_br0500) Dobson (10.1016/j.cpc.2021.108171_br2470) 2014; 141 Jaramillo-Botero (10.1016/j.cpc.2021.108171_br0420) 2011; 32 Pollock (10.1016/j.cpc.2021.108171_br0250) 1996; 95 Milano (10.1016/j.cpc.2021.108171_br1290) 2005; 109 Malevanets (10.1016/j.cpc.2021.108171_br1390) 1999; 110 Lysogorskiy (10.1016/j.cpc.2021.108171_br1810) 2021; 7 Tribello (10.1016/j.cpc.2021.108171_br2430) 2014; 185 Zuo (10.1016/j.cpc.2021.108171_br1080) 2020; 124 Nguyen (10.1016/j.cpc.2021.108171_br1490) 2019; 243 Auhl (10.1016/j.cpc.2021.108171_br2550) 2003; 119 Fattebert (10.1016/j.cpc.2021.108171_br0880) 2012; 183 Noid (10.1016/j.cpc.2021.108171_br1330) 2008; 128 Shapeev (10.1016/j.cpc.2021.108171_br1780) 2016; 14 Phillips (10.1016/j.cpc.2021.108171_br0390) 2010; 133 Shell (10.1016/j.cpc.2021.108171_br1270) 2016; 161 Ma (10.1016/j.cpc.2021.108171_br2270) 2008; 78 Jaime (10.1016/j.cpc.2021.108171_br2190) 2017; 8 Plimpton
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Title	LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales
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