A deep potential model with long-range electrostatic interactions

TL;DR

This paper introduces a deep potential model that explicitly incorporates long-range electrostatic interactions using Gaussian charge distributions, enhancing accuracy and scalability in molecular simulations.

Contribution

The authors extend the deep potential model by integrating a rigorous long-range electrostatics component based on Gaussian charges and Wannier centers, improving predictive power for large systems.

Findings

Accurately models water dimer potential energy profile.

Predicts free energy of water interaction with liquid water.

Reproduces phonon dispersion curves of NaCl crystal.

Abstract

Machine learning models for the potential energy of multi-atomic systems, such as the deep potential (DP) model, make possible molecular simulations with the accuracy of quantum mechanical density functional theory, at a cost only moderately higher than that of empirical force fields. However, the majority of these models lack explicit long-range interactions and fail to describe properties that derive from the Coulombic tail of the forces. To overcome this limitation we extend the DP model by approximating the long-range electrostatic interaction between ions (nuclei+core electrons) and valence electrons with that of distributions of spherical Gaussian charges located at ionic and electronic sites. The latter are rigorously defined in terms of the centers of the maximally localized Wannier distributions, whose dependence on the local atomic environment is modeled accurately by a deep neural network. In the deep potential long-range (DPLR) model, the electrostatic energy of the Gaussian charge system is added to short-range interactions that are represented as in the standard DP model. The resulting potential energy surface is smooth and possesses analytical forces and virial. Missing effects in the standard DP scheme are recovered, improving on accuracy and predictive power. By including long-range electrostatics, DPLR correctly extrapolates to large systems the potential energy surface learned from quantum mechanical calculations on smaller systems. We illustrate the approach with three examples, the potential energy profile of the water dimer, the free energy of interaction of a water molecule with a liquid water slab, and the phonon dispersion curves of the NaCl crystal.