LAMMPS 

Description

LAMMPS is a classical molecular dynamics code, and an acronym for Large-scale Atomic/Molecular Massively Parallel Simulator. LAMMPS has potentials for solid-state materials (metals, semiconductors) and soft matter (biomolecules, polymers) and coarse-grained or mesoscopic systems. It can be used to model atoms or, more generically, as a parallel particle simulator at the atomic, meso, or continuum scale. LAMMPS runs on single processors or in parallel using message-passing techniques and a spatial-decomposition of the simulation domain. The code is designed to be easy to modify or extend with new functionality.

More information

- Homepage: https://www.lammps.org

Available Versions of LAMMPS

This table is generated automatically. If you need other versions please click pc2-support@uni-paderborn.de.

Usage Hints for LAMMPS

If you need support in using this software or example job scipts please contact pc2-support@uni-paderborn.de.

Noctua 2

General Usage

For using LAMMPS on Noctua 2 you can use the following input file as a template. It performs at 1,074 ns/day for 2000 steps of the Rhodopsion benchmark with 2.048.000 atoms. It is slightly faster than other published results with 64-core AMD Milan CPUs (see https://dl.acm.org/doi/pdf/10.1145/3437359.3465596).

#!/bin/bash
#SBATCH -N 1
#SBATCH --exclusive
#SBATCH --ntasks-per-node=128
#SBATCH -t 30:00

module reset
# Note: please select one of the installed LAMMPS modules that is suitable for your simulation.
module load chem/LAMMPS/3Mar2020-foss-2020a-Python-3.8.2-kokkos

export OMP_NUM_THREADS=1
export OPENBLAS_NUM_THREADS=1

srun lmp -in rhodo.inp

Use Special Packages in LAMMPS

ML-HDNNP Package

The high-dimensional neural network potential (HDNNP) method (Behler and Parrinello 2007) can provide machine learning potentials for MD simulations. If your calculation uses HDNNP in LAMMPS, please load the module chem/LAMMPS/2Aug2023_update3-foss-2023b-ml_hdnnp-kokkos in your Slurm jobscript.

If you experience problems, need support for other versions or performance tuning to a certain case, please let us know at pc2-support@uni-paderborn.de.