Accelerating QM/MM simulations of electrochemical interfaces through machine learning of electronic charge densities

TL;DR

This paper integrates a machine learning model for electronic charge density with classical electrochemical simulations, enabling faster and more accurate modeling of electrochemical interfaces at nanosecond timescales.

Contribution

It introduces a spherical harmonics extension of Ewald summation to efficiently incorporate ML-predicted charge densities into electrochemical simulations.

Findings

Achieved small force errors (~1 meV/Å) demonstrating high accuracy.

Enabled nanosecond-scale simulations of electrochemical interfaces.

Observed qualitative differences in electrolyte distribution compared to classical models.

Abstract

A crucial aspect in the simulation of electrochemical interfaces consists in treating the distribution of electronic charge of electrode materials that are put in contact with an electrolyte solution. Recently, it has been shown how a machine-learning method that specifically targets the electronic charge density, also known as SALTED, can be used to predict the long-range response of metal electrodes in model electrochemical cells. In this work, we provide a full integration of SALTED with MetalWalls, a program for performing classical simulations of electrochemical systems. We do so by deriving a spherical harmonics extension of the Ewald summation method, which allows us to efficiently compute the electric field originated by the predicted electrode charge distribution. We show how to use this method to drive the molecular dynamics of an aqueous electrolyte solution under the quantum electric field of a gold electrode, which is matched to the accuracy of density-functional theory. Notably, we find that the resulting atomic forces present a small error of the order of 1 meV/{\AA}, demonstrating the great effectiveness of adopting an electron-density path in predicting the electrostatics of the system. Upon running the data-driven dynamics over about 3 ns, we observe qualitative differences in the interfacial distribution of the electrolyte with respect to the results of a classical simulation. By greatly accelerating quantum-mechanics/molecular-mechanics approaches applied to electrochemical systems, our method opens the door to nanoseconds timescales in the accurate atomistic description of the electrical double layer.