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 for carbon and hydrogen that are used to reproduce the target properties: n-octane's relative conformational energies and its bulk-phase density. By substituting the most time-consuming element of this optimization, the required time is reduced by a factor of ≈20, while retaining FFs with similar quality. Furthermore, the workflow used to obtain the surrogate model (i.e., training data acquisition, data preparation, model selection, and training) for such substitution is presented.