Python(编程语言)
计算机科学
Fortran语言
计算科学
分子动力学
原子模型
灵活性(工程)
源代码
巨量平行
程序设计语言
并行计算
物理
数学
量子力学
统计
作者
Aidan P. Thompson,Hasan Metin Aktulga,Richard Berger,Dan Bolintineanu,William M. Brown,Paul Crozier,Pieter J. in ’t Veld,Axel Kohlmeyer,Stan Moore,Trung Dac Nguyen,Ray Shan,Mark J. Stevens,Julien Tranchida,Christian Robert Trott,Steven J. Plimpton
标识
DOI:10.1016/j.cpc.2021.108171
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI