Valence fitting

This repository is a small modification of scripts at https://github.com/openforcefield/sage-2.1.0.

Create the environment necessary with the below. Replace micromamba with mamba or conda as needed.

micromamba env create -f environment.yaml

If you're running on Apple Silicon, some packages are still not available for ARM yet. Instead, do the following:

CONDA_SUBDIR=osx-64 micromamba env create -f environment.yaml
micromamba activate fb-195-tk-014-py310
conda config --env --set subdir osx-64  # make sure updates keep using the subdir

The general steps laid out here are:

1. Generate the initial force field for fitting. This step defines the SMIRKS of each parameter that will be fit. When adding, removing, or modifying parameters in force fields, here is where it should be changed.
2. Download and curate QM datasets. This step downloads QM datasets; manually adds additional data records; and manually removes bad records. For torsions, you can specify whether to cap the number of torsion data records per torsion parameter for fitting. For optimizations, you can cap the number of conformers used as well. The output of these scripts are the QM datasets, as well as which parameters will be trained. A parameter will not be trained if there isn't enough data coverage (also specified by the user).
3. This step generates initial values for each force field parameter using the Modified Seminario Method.
4. This step sets up the final ForceBalance input files for training.

After setting up ForceBalance, the scripts directory contains scripts suitable for running the fit on HPC3.

The included Makefile can be used to run the whole pipeline at once:

make step4

Alternatively, one of the preceding steps can be run directly. make will handle generating any required dependencies. For example,

make step3

will generate 03_generate-initial-ff/output/initial-force-field-msm.offxml, which depends on 01_generate-forcefield/output/initial-force-field-openff-2.1.0.offxml from step 1, 02_curate-data/output/combined-opt.json from step 2, and the script 03_generate-initial-ff/create-msm-ff.py from step 3. These files from steps 1 and 2 in turn depend on other input files and scripts, which will be rerun if their own dependencies change.

Dependency graph