NNP/CG-MM: Embedding of All-Atom Neural Network Potentials into a Coarse-Grained Molecular Mechanics Environment

Abstract

Neural network potentials (NNPs), or neural network-based force fields, are gaining widespread attention for their ability to model complex chemical, materials, and biophysical systems. In NNPs, the total energy of the system can be decomposed into atom-centered components, where the energies and forces are described using a deep neural network. However, despite the flexibility and accuracy of NNPs, they are typically more computationally intensive than classical molecular dynamics. There have been recent advances in embedding NNPs into a molecular mechanics (MM)-based environment to maximize efficiency while retaining the accuracy of NNPs. In this work, we propose a method called NNP/CG-MM, in which an all-atom NN force field is systematically embedded into a coarse-grained molecular mechanics environment (CG-MM). Coarse-graining (CG) involves constructing a simplified representation of a larger fine-grained (FG) system with the goal of significantly accelerating computations while maintaining the accuracy of the FG system when projected onto the CG variable distributions. The NNP-CG coupling terms are constructed using the multiscale CG force-matching (MS-CG) method. The scheme is tested on liquids and in capturing features of the hydrophobic effect, where three-body correlations in the CG solvent can play an important role.