Exploring the Evolution of Water Structures on ZnO Surfaces Using Deep Learning

H Liang and CH Ye and T Zeng and JZ Zhang and XK Gu, JOURNAL OF PHYSICAL CHEMISTRY C, 129, 20436-20445 (2025).

DOI: 10.1021/acs.jpcc.5c06131

The interactions between water molecules and ZnO surfaces play a critical role in thermochemical, photochemical, and electrochemical processes, particularly in proton transfer reactions. However, in practical applications, the presence of water chains or clusters formed by adsorbed water molecules can obscure the actual adsorption configurations of other competing reactants. Therefore, a comprehensive understanding of single and dimer water dissociation, the formation of water chains and clusters, and the influence of surface reconstruction is essential for advancing ZnO-based interfacial catalysis. This study develops accurate neural network potentials (NNPs) for both polar and nonpolar ZnO surfaces and water molecules, based on ab initio molecular dynamics (AIMD) data. These NNPs enable efficient, long-time simulations with reduced computational costs. We identified distinct water dissociation mechanisms on ZnO surfaces and simulated a 50% dissociation rate, in agreement with experimental results. The NNPs also revealed the formation of water chains and clusters on ZnO surfaces. Additionally, we explored the dissociation behavior of the polar ZnO surface, which undergoes structural deformation, adopting a configuration similar to the nonpolar surface, although the dissociation degree remains lower. These findings provide valuable insights into interfacial water behavior on ZnO for catalysis and material applications.

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