hfcs-fffit is a repository used for a case study of our machine learning directed force field optimization workflow outlined in this preprint. Here we use the procedure to generate force fields for two hydrofluorocarbons (HFCs), HFC-32 and HFC-125.

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

The non-dominated and "top four" parameter sets for each HFC are provided under hfcs-fffit/analysis/csv/. The non-dominated sets are found in r32-pareto.csv and r125-pareto.csv, and the "top four" sets are found in r32-final-4.csv and r125-final-4.csv. The parameter values in the csv files are normalized between 0 and 1 based upon the parameter bounds for each atom type (see manuscript, or hfcs-fffit/analysis/utils.r32.py and hfcs-fffit/analysis/utils.r125.py for definitions).

All molecular simulations were performed under hfcs-fffit/runs. Each iteration was managed with signac-flow. Inside of each directory in runs, you will find all the necessary files to run the simulations. Note that you may not get the exact same simulation results due to differences in software versions, random seeds, etc. Nonetheless, all of the results from our molecular simulations are saved under hfcs-fffit/analysis/rXX-YY-iterZZ-results.csv, where XX is the molecule, YY is the stage (liquid density or VLE), and ZZ is the iteration number.

All of the scripts for the surrogate modeling are provided in hfcs-fffit/analysis, following the same naming structure as the csv files.

All scripts required to generate the primary figures in the manuscript are reported under hfcs-fffit/analysis/final-figs and the associated PDF files are located under hfcs-fffit/analysis/final-figs/pdfs.

This package has a variety of requirements that can be installed in different ways. We recommend using a conda environment to manage most of the installation and dependencies, and installing some items from source or pip.

We recommend starting with a fresh conda environment, then installing the packages listed under requirements-pip.txt with pip, then installing the packages listed under requirements-conda.txt with conda, and finally installing a few items from source requirements-other.txt. We recommend python3.7 and taking packages from conda-forge.

This work 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.