Developing machine-learned potentials to simultaneously capture the dynamics of excess protons and hydroxide ions in classical and path integral simulations

TL;DR

This paper introduces machine-learned potentials that accurately simulate proton and hydroxide ion transport in water, capturing quantum effects and bond dynamics at a fraction of ab initio computational costs.

Contribution

The authors develop machine-learned potentials capable of modeling proton and hydroxide transport with ab initio accuracy, enabling long-timescale simulations including nuclear quantum effects.

Findings

MLPs reproduce ab initio trends in ion diffusion

Simulations reveal hypercoordination's role in hydroxide transport

Asymmetry in diffusion between protons and hydroxide ions

Abstract

The transport of excess protons and hydroxide ions in water underlies numerous important chemical and biological processes. Accurately simulating the associated transport mechanisms ideally requires utilizing ab initio molecular dynamics simulations to model the bond breaking and formation involved in proton transfer and path-integral simulations to model the nuclear quantum effects relevant to light hydrogen atoms. These requirements result in a prohibitive computational cost, especially at the time and length scales needed to converge proton transport properties. Here, we present machine-learned potentials (MLPs) that can model both excess protons and hydroxide ions at the generalized gradient approximation and hybrid density functional theory levels of accuracy and use them to perform multiple nanoseconds of both classical and path-integral proton defect simulations at a fraction of the cost of the corresponding ab initio simulations. We show that the MLPs are able to reproduce ab initio trends and converge properties such as the diffusion coefficients of both excess protons and hydroxide ions. We use our multi-nanosecond simulations, which allow us to monitor large numbers of proton transfer events, to analyze the role of hypercoordination in the transport mechanism of the hydroxide ion and provide further evidence for the asymmetry in diffusion between excess protons and hydroxide ions.