Atomistic Simulations of Oxide-Water Interfaces using Machine Learning Potentials

TL;DR

This paper reviews recent advances in applying machine learning potentials to simulate oxide-water interfaces, capturing complex interfacial phenomena with near ab initio accuracy at reduced computational costs.

Contribution

It provides a comprehensive overview of how machine learning potentials are used to model oxide-water interfaces, highlighting recent progress and future challenges.

Findings

Insights into water dissociation and recombination at interfaces

Understanding proton transfer mechanisms

Characterization of dynamic oxide-water surface interactions

Abstract

Oxide-water interfaces govern a wide range of physical and chemical processes fundamental to many fields like catalysis, geochemistry, corrosion, electrochemistry, and sensor technology. Near solid oxide surfaces, water behaves differently than in the bulk, exhibiting pronounced structuring and increased reactivity, typically requiring ab initio-level accuracy for reliable modeling. However, explicit ab initio calculations are often computationally prohibitive, especially if large system sizes and long simulation time scales are required. By learning the potential energy surface (PES) from data obtained from electronic structure calculations, machine learning potentials (MLPs) have emerged as transformative tools, enabling simulations with ab initio accuracy at dramatically reduced computational expense. Here, we provide an overview of recent progress in the application of MLPs to atomistic simulations of oxide-water interfaces. Specifically, we review insights that have been gained into the reactivity of interfacial systems involving the dissociation and recombination of water molecules, proton transfer processes between the solvent and the surface and the dynamic nature of aqueous oxide surfaces. Moreover, we discuss open challenges and future possible research directions in this rapidly evolving but challenging field.