A neural network potential based on pairwise resolved atomic forces and energies

Abstract

Molecular simulations have become a key tool in molecular and materials design. Machine learning (ML)‐based potential energy functions offer the prospect of simulating complex molecular systems efficiently at quantum chemical accuracy. In previous work, we have introduced the ML‐based PairF‐Net approach to neural network potentials, that adopts a pairwise interatomic scheme to predicting forces within a molecular system. Here, we further develop the PairF‐Net model to intrinsically incorporate energy conservation and couple the model to a molecular mechanical (MM) environment within the OpenMM package. The updated PairF‐Net model yields energy and force predictions and dynamical distributions in good agreement with the rMD17 dataset of ten small organic molecules in the gas‐phase. We further show that these in vacuo ML models of small molecules can be applied to force predictions in aqueous solution via hybrid ML/MM simulations. We present a new benchmark dataset for these ten molecules in solution, obtained from QM/MM simulations, which we denote as rMD17‐aq (https://zenodo.org/records/10048644); and assess the ability of PairF‐Net to reproduce the molecular energy, atomic forces and dynamical distributions of these solution conformations via ML/MM simulations.