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.
JO  - Computer Physics Communications
VL  - 271
SP  - 108171
PY  - 2022
DA  - 2022/02/01/
SN  - 0010-4655
DO  - https://doi.org/10.1016/j.cpc.2021.108171
UR  - https://www.sciencedirect.com/science/article/pii/S0010465521002836
KW  - Molecular dynamics
KW  - Materials modeling
KW  - Parallel algorithms
KW  - LAMMPS
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.
ER  - 

TY  - JOUR
T1  - Fast Parallel Algorithms for Short-Range Molecular Dynamics
AU  - Plimpton, Steve
JO  - Journal of Computational Physics
VL  - 117
IS  - 1
SP  - 1
EP  - 19
PY  - 1995
DA  - 1995/03/01/
SN  - 0021-9991
DO  - https://doi.org/10.1006/jcph.1995.1039
UR  - https://www.sciencedirect.com/science/article/pii/S002199918571039X
AB  - Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.
ER  -