New insights into anisotropic dynamics at the Pt(211)/water interface revealed by machine learning molecular dynamics

A research team from the University of Xiamen has developed a machine learning potential specifically for Pt-water interfaces. This research harnessed machine learning molecular dynamics to uncover the intricated interactions of Pt(211)/water interface. The simulations provided detailed insights into the structure and dynamics of water molecules at the Pt/water interface, revealing distinct types of water molecules and their anisotropic behavior. This understanding is crucial for elucidating interfacial processes such as water dissociation and ion solvation, which are fundamental in electrochemical reactions.

The stepped Pt surfaces exhibit higher electrocatalytic performance than the basal Pt planes. Stepped surfaces contain a higher density of low-coordination atoms (like edge and corner atoms) compared to basal planes. These low-coordination sites are more chemically reactive, facilitating stronger adsorption and more favorable interaction with reactant molecules, leading to enhanced catalytic activity. On the other hand, the organization and behavior of the interfacial water molecules can significantly influence the solvation of ions, the adsorption of reactants, and the kinetics of electrochemical reactions. Revealing the in-situ details of water structures at these stepped Pt/water interfaces is crucial for understanding the fundamental mechanisms that drive diverse applications in energy conversion and material science. Nevertheless, it is still a grand challenge to determine the interfacial water structures with high temporal and spatial resolution at ambient condition. Ab initio molecular dynamics simulations provide a way to study the interfacial structures but are limited by the high computation cost. Therefore, developing efficient method with high accuracy to conduct molecular dynamics simulations is essential to explore the molecular basis for many interfacial phenomena.

The Solution: A team from the University of Xiamen reported a machine learning molecular dynamics (MLMD) simulations of the Pt(211)/water interfaces. The results reveal distinct types of chemisorbed and physisorbed water molecules within the adsorbed layer. Three unique water pairs are observed between these adsorbed water molecules, which may serve as key precursors for water dissociation. These interfacial water structures contribute to the anisotropic dynamics (diffusion, reorientation and hydrogen bond dynamics) of the adsorbed water layer. This work provides a theoretical support for the experimental study of the in-situ interfacial structures and dynamics.

The Future: Future research will explore dynamic properties at longer timescales at the stepped metal/water interfaces and establish connections to the experimental observables.

The Impact: This work offers a promising way to understand the anisotropic properties at stepped metal/water interfaces from the prominent water pairs formed at the interface.

The research has been recently published in the online edition of Materials Futures, a new international journal in the field of interdisciplinary materials science research.

Reference:

Fei-Teng Wang, Xiandong Liu, Jun Cheng. Water Structures and Anisotropic Dynamics at Pt(211)/Water Interface Revealed by Machine Learning Molecular Dynamics[J]. Mater. Futures 3 041001