lammps-polymlp-package

A package of LAMMPS software enabling simulations using polynomial machine learning potentials

Building lammps with lammps-polymlp package

(lammps-polymlp-package is tested using LAMMPS_VERSION "lammps-23Jun2022”)

1. Copy all the components in the lammps-polymlp-package to the latest lammps source code directory as

   > cp -r lammps-polymlp-package/lib/polymlp $(lammps_src)/lib
   > cp -r lammps-polymlp-package/src/POLYMLP $(lammps_src)/src
   

2. Add "polymlp" to variable PACKAGE defined in $(lammps_src)/src/Makefile and activate polymlp package as

   > cat $(lammps_src)/src/Makefile
       PACKAGE = \
       adios \
       amoeba \
       ...
       poems \
       polymlp \
       ptm \
       ...
       ml-iap \
       phonon
       ...
   > ulimit -s unlimited
   > cd $(lammps_src)/src
   > make yes-polymlp
   

3. If necessary, modify compile options and flags in $(lammps_src)/src/MAKE/Makefile.serial and $(lammps_src)/lib/polymlp/Makefile.lammps

4. Change directory to $(lammps_src)/src and build lammps binary files. (It requires approximately ten minutes to one hour for compiling polymlp_gtinv_data.cpp.)

   > make serial -j 36
   

Note that MPI is not currently available for the lammps-polymlp package, but OpenMP is supported. Even when using the OpenMP-supported lammps-polymlp package, you should compile LAMMPS using the command make serial. Parallel calculations can be performed using OpenMP with lmp_serial.

Machine learning potentials for a wide range of systems can be found in the website. If you use lammps-polymlp package and machine learning potentials in the repository for academic purposes, please cite the following article [1].

[1] A. Seko, "Systematic development of polynomial machine learning potentials for elemental and alloy systems", J. Appl. Phys. 133, 011101 (2023).

Lammps input commands to specify a machine learning potential

The following lammps input commands specify a machine learning potential.

    pair_style  polymlp
    pair_coeff * * polymlp.lammps Ti Al

If you are using a polynomial machine learning potential generated by pypolymlp (version >= 0.9.0), the potential can be specified as follows:

    pair_style  polymlp
    pair_coeff * * polymlp.yaml Ti Al