Evolutionary Machine Learning of Physics-Based Force Fields in High-Dimensional Parameter-Space

19 March 2025, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

This work presents the Alexandria Chemistry Toolkit (ACT), an open-source software for machine learning of physics-based force fields (FFs) from scratch, based on user-specified potential functions. In this approach, a set of FF parameters for molecular simulation is described as a chromosome consisting of atom and bond genes. The accuracy of a FF, that is how well quantum chemical train- ing data are reproduced, determines the fitness of the chromosome. The ACT implements a hierarchical parallel scheme that iterates between a genetic algorithm and Monte-Carlo steps for global and local search, to find “genomes” with high fitness. As a sample appli- cation, genome evolution is performed to create physical models that allow the prediction of properties of organic molecules in the gas and liquid phases. Evaluation of the prediction accuracy of different models showcases how Force Field Science can contribute to system- atically improve prediction accuracy of physicochemical observables.

Keywords

Machine learning
Force Fields
Big Data
Physical Chemistry
Molecular Simulation

Supplementary weblinks

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