Improved force field accuracy through training against physical properties of mixed systems

This repository contains the scripts, inputs and the results generated as part of the Improved force field accuracy through training against physical properties of mixed systems publication.

Structure

This repository is structured into four main directories:

• data-set-curation - contains the script used to curate the training and test data sets.

• inputs-and-results - contains the most up to date input files required to reproduce this study. See the Reproduction section of this README for more information. The project structure was for the most part generated automatically using the nonbonded package.

• schema - contains the nonbonded schemas which define the entirety of the project, including definitions of the which optimizations and benchmarks to be performed and their respective training and test data sets.

• scripts - contains the script used to curate the training and test data sets, generate the input nonbonded schemas, and scripts which perform ancillary data analysis.

Experimental Data Sets

The experimental data sets used in this project were curated from the NIST ThermoML archive. The citations for the individual measurements can be found in DATA-CITATIONS.bib

Reproduction

The exact inputs used and outputs reported (including the conda environment used to generate them) in the publication have been included as tagged releases to this repository.

For those looking to reproduce the study, the required dependencies may be obtained directly using conda:

conda env create --name binary-mixture-publication --file environment.yaml

Optimizations

In most cases the optimizations can be re-run using the following commands

cd inputs-and-results/optimizations/XXX/
nonbonded optimization run
nonbonded optimization analyze

where XXX is the unique mnemonic associated with a particular optimization.

Benchmarks

In most cases the benchmarks can be re-run using the following commands

cd inputs-and-results/benchmarks/XXX/
nonbonded optimization run
nonbonded optimization analyze

where XXX is the unique mnemonic associated with a particular benchmark.