Explicit, Machine-Learned Two-Body Potentials for Molecular Simulations

TL;DR

This paper introduces a hybrid machine-learning and molecular mechanics potential for large condensed-phase systems, combining PhysNet ML models with classical force fields to improve accuracy in molecular simulations.

Contribution

It presents a novel hybrid ML/MM approach that models short-range interactions with PhysNet and long-range with classical force fields, tailored for heterogeneous condensed-phase systems.

Findings

Accurately describes pairwise reference data for dichloromethane and acetone.

Demonstrates applicability in molecular dynamics simulations of small systems.

Highlights limitations due to many-body effects, suggesting future improvements.

Abstract

A new pairwise hybrid machine-learning/molecular mechanics (ML/MM) potential is introduced that is conceived for application to large, heterogeneous condensed-phase systems. The PhysNet ML method describes monomers and short-range dimer interactions, while a classical MM force field describes pairwise interactions beyond a defined switching distance. Models are fitted to MP2 dimer and pairwise cluster energies, and the quality of each model is assessed at different switching distances and using MM approaches with and without detailed distributed charge electrostatics. The applicability of the approach to molecular dynamics simulations is demonstrated for a basic implementation applied to a small model system. Dichloromethane and acetone are used as test systems to demonstrate the accuracy of the approach in describing pairwise reference data, and also to highlight the limitations of the pairwise approach for systems that exhibit significant many-body effects in condensed phase, paving the way for the addition of a general many-body correction in future work.