Speed up Multi-Scale Force-Field Parameter Optimization by Substituting Molecular Dynamics Calculations with a Machine Learning Surrogate Model.

ABSTRACT: Molecular modeling plays a vital role in many scientific fields, ranging from material science to drug design. To predict and investigate the properties of those systems, a suitable force field (FF) is required. Improving the accuracy or expanding the applicability of the FFs is an ongoing process, referred to as force-field parameter (FFParam) optimization. In recent years, data-driven machine learning (ML) algorithms have become increasingly relevant in computational sciences and elevated the capability of many molecular modeling methods. Herein, time-consuming molecular dynamic simulations, used during a multiscale FFParam optimization, are substituted by a ML surrogate model to speed-up the optimization process. Subject to this multiscale optimization are the Lennard-Jones parameters f

SUBMITTER: Strickstrock R 

PROVIDER: S-EPMC12530876 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature