Investigating water structure and dynamics at metal/water interfaces from classical, ab initio to machine learning molecular dynamics

Abstract

Metal-water interfaces are central to a wide range of crucial processes, including energy storage, energy conversion, and corrosion. Understanding the detailed structure and dynamics of water molecules at these interfaces is essential for unraveling the fundamental mechanisms driving these processes at the molecular level. Experimentally, a detection of interfacial structure and dynamics with high temporal and spatial resolution is lacking. The advances in machine learning molecular dynamics are offering an opportunity to address this issue with high accuracy and efficiency. To offer insights into the structure and dynamics, this review summarizes the progress made in determining the structure and dynamics of interfacial water molecules using molecular dynamics simulations. The possible application of machine learning molecular dynamics to address the fundamental challenges of simulating metal/water interfaces are also discussed.