A Python package for machine learning potentials with LAMMPS.
Documentation page: https://fitsnap.github.io
Colab Python notebook tutorial: https://colab.research.google.com/github/FitSNAP/FitSNAP/blob/master/tutorial.ipynb
Rohskopf et al., (2023). FitSNAP: Atomistic machine learning with LAMMPS. Journal of Open Source Software, 8(84), 5118, https://doi.org/10.21105/joss.05118
pyproject.toml
import lammps; lmp = lammps.lammps()
without errors in your Python interpreter,
you're good to go!mpi4py
. If installing mpi4py with a Python package manager, we recommend using
pip over conda as pip will auto-configure your package to your system's defaut MPI version
(usually what you used to build LAMMPS).WARNING: Conda LAMMPS installation does NOT include ACE. See the docs for details on how to install the current LAMMPS which has these functionalities.
conda config --add channels conda-forge
conda create -n fitsnap python=3.9; conda activate fitsnap;
conda install -c conda-forge lammps fitsnap3
(mpirun -np #) python -m fitsnap3 [options] infile
python -m fitsnap3 -h
examples/
examples/library
flake8
and running flake8 --statistics
in the top
directory.pip install sphinx sphinx_rtd_theme
for adding new documentation, and see docs/README.md
for how to build docs for your features. Copyright (2016) Sandia Corporation. Under the terms of Contract DE-AC04-94AL85000 with Sandia Corporation, the U.S. Government retains certain rights in this software. This software is distributed under the GNU General Public License