1.1. Overview of LAMMPS

LAMMPS is a classical molecular dynamics (MD) code that models ensembles of particles in a liquid, solid, or gaseous state. It can model atomic, polymeric, biological, solid-state (metals, ceramics, oxides), granular, coarse-grained, or macroscopic systems using a variety of interatomic potentials (force fields) and boundary conditions. It can model 2d or 3d systems with only a few particles up to millions or billions.

LAMMPS can be built and run on a laptop or desktop machine, but is designed for parallel computers. It will run in serial and on any parallel machine that supports the MPI message-passing library. This includes shared-memory boxes and distributed-memory clusters and supercomputers. Parts of LAMMPS also support OpenMP multi-threading, vectorization and GPU acceleration.

LAMMPS is written in C++ and requires a compiler that is at least compatible with the C++-11 standard. Earlier versions were written in F77, F90, and C++-98. See the History page of the website for details. All versions can be downloaded as source code from the LAMMPS website.

LAMMPS is designed to be easy to modify or extend with new capabilities, such as new force fields, atom types, boundary conditions, or diagnostics. See the Modify page for more details.

In the most general sense, LAMMPS integrates Newton’s equations of motion for a collection of interacting particles. A single particle can be an atom or molecule or electron, a coarse-grained cluster of atoms, or a mesoscopic or macroscopic clump of material. The interaction models that LAMMPS includes are mostly short-range in nature; some long-range models are included as well.

LAMMPS uses neighbor lists to keep track of nearby particles. The lists are optimized for systems with particles that are repulsive at short distances, so that the local density of particles never becomes too large. This is in contrast to methods used for modeling plasma or gravitational bodies (e.g. galaxy formation).

On parallel machines, LAMMPS uses spatial-decomposition techniques with MPI parallelization to partition the simulation domain into small sub-domains of equal computational cost, one of which is assigned to each processor. Processors communicate and store “ghost” atom information for atoms that border their sub-domain. Multi-threading parallelization and GPU acceleration with with particle-decomposition can be used in addition.