Regularized by Physics: Graph Neural Network Parametrized Potentials for the Description of Intermolecular Interactions.

ABSTRACT: Simulations of molecular systems using electronic structure methods are still not feasible for many systems of biological importance. As a result, empirical methods such as force fields (FF) have become an established tool for the simulation of large and complex molecular systems. The parametrization of FF is, however, time-consuming and has traditionally been based on experimental data. Recent years have therefore seen increasing efforts to automatize FF parametrization or to replace FF with machine-learning (ML) based potentials. Here, we propose an alternative strategy to parametrize FF, which makes use of ML and gradient-descent based optimization while retaining a functional form founded in physics. Using a predefined functional form is shown to enable interpretability, robustness, an

SUBMITTER: Thurlemann M 

PROVIDER: S-EPMC9878731 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature