Anisotropic molecular coarse-graining by force and torque matching with neural networks

TL;DR

This paper introduces a neural network-based anisotropic coarse-graining method for molecular systems, enabling accurate and computationally efficient modeling of complex anisotropic interactions and phase behaviors.

Contribution

It extends neural network potentials to anisotropic particles, allowing flexible, accurate coarse-grained models for complex molecules with many-body effects.

Findings

Achieves structural accuracy close to all-atom models

Captures anisotropic interactions and many-body effects

Reproduces phase transitions over wide temperature ranges

Abstract

We develop a machine-learning method for coarse-graining condensed-phase molecular systems using anisotropic particles. The method extends currently available high-dimensional neural network potentials by addressing molecular anisotropy. We demonstrate the flexibility of the method by parametrizing single-site coarse-grained models of a rigid small molecule (benzene) and a semi-flexible organic semiconductor (sexithiophene), attaining structural accuracy close to the all-atom models for both molecules at considerably lower computational expense. The machine-learning method of constructing the coarse-grained potential is shown to be straightforward and sufficiently robust to capture anisotropic interactions and many-body effects. The method is validated through its ability to reproduce the structural properties of the small molecule's liquid phase and the phase transitions of the semi-flexible molecule over a wide temperature range.