Accurate nuclear quantum statistics on machine-learned classical effective potentials

TL;DR

This paper introduces a machine learning approach to efficiently incorporate nuclear quantum effects into molecular simulations, significantly reducing computational costs while maintaining high accuracy across various systems.

Contribution

The authors develop a machine-learned potential that accurately captures nuclear quantum effects, enabling efficient and transferable simulations of complex molecular systems.

Findings

Machine-learned potential closely matches PIMD results

Significant reduction in computational cost

Applicable to diverse molecular systems

Abstract

The contribution of nuclear quantum effects (NQEs) to the properties of various hydrogen-bound systems, including biomolecules, is increasingly recognized. Despite the development of many acceleration techniques, the computational overhead of incorporating NQEs in complex systems is sizable, particularly at low temperatures. In this work, we leverage deep learning and multiscale coarse-graining techniques to mitigate the computational burden of path integral molecular dynamics (PIMD). Specifically, we employ a machine-learned potential to accurately represent corrections to classical potentials, thereby significantly reducing the computational cost of simulating NQEs. We validate our approach using four distinct systems: Morse potential, Zundel cation, single water molecule, and bulk water. Our framework allows us to accurately compute position-dependent static properties, as demonstrated by the excellent agreement obtained between the machine-learned potential and computationally intensive PIMD calculations, even in the presence of strong NQEs. This approach opens the way to the development of transferable machine-learned potentials capable of accurately reproducing NQEs in a wide range of molecular systems.