fffit is a Python package for fitting molecular mechanics force fields to experimental properties by using Gaussian process surrogate models to rapidly screen through large parameter spaces.
fffit is still in early development (0.x releases). The API may change unexpectedly.
Example repositories have used this package for force field development for hydrofluorocarbons and ammonium perchlorate. See those repositories for further details of how you can use this package in your work.
This work has been submitted for review. In the meantime, you may cite the preprint as:
BJ Befort, RS DeFever, G Tow, AW Dowling, and EJ Maginn. Machine learning directed optimization of classical molecular modeling force fields. arXiv (2021), https://arxiv.org/abs/2103.03208
Installation is currently only available from source. We recommend installing the package within a dedicated venv or conda environment. Here we demonstrate with a venv environment:
git clone https://github.com/rsdefever/fffit.git
cd fffit/
python3 -m venv fffit-env
source fffit-env/bin/activate
python3 -m pip install -r requirements.txt
pip install -e .Note this will make an editable installation so that any of the changes
you make in your fffit.
More information on virtual environments can be found here.
Development of fffit was supported by the National Science Foundation under grant NSF Award Number OAC-1835630 and NSF Award Number CBET-1917474. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.