This project contains fits and LAMMPS input scripts for benchmarking accuracy and performance of ML potentials.
The following directories for fits are organized like:
<potential>
<system>
E.g. <potential> can be SNAP, ACE, etc. and <system> can be Si, Li, Mo, etc.
Training data is stored in the data directory.
First activate your Python environment, e.g.
source ~/venv-fitsnap-pace/bin/activate
Proceed into a <potential>/<system> directory and run a fit with:
mpirun -np 4 python -m fitsnap3 input.in --overwrite
Obtain CPU benchmarks on a 10x10x10 unit cell system with:
mpirun -np P lmp -in in.run -v nrep 10
Obtain GPU/Kokkos benchmarks on a 10x10x10 unit cell system with (e.g. using 2 GPUs):
mpirun -np 2 lmp -k on g 2 -sf kk -pk kokkos newton on neigh half -in in.run -v nrep 10