Regensburg 2025 – scientific programme

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O: Fachverband Oberflächenphysik

O 34: Poster Solid-Liquid Interfaces: Reactions and Electrochemistry

O 34.6: Poster

Tuesday, March 18, 2025, 13:30–15:30, P3

Investigating Zinc Oxide-Water Interfaces with High-Dimensional Neural Network Potentials — •Jan Elsner and Jörg Behler — Theoretische Chemie II, Ruhr-Universität Bochum, Germany, and ResearchCenter Chemical Sciences and Sustainability, Research Alliance Ruhr,Germany

Zinc oxide (ZnO) is a promising material for sustainable hydrogen production via catalytic water splitting. The interface of ZnO with water exhibits complex dynamical behavior, including water dissociation and recombination, as well as long-range proton transport. Traditionally, density functional theory (DFT)-based molecular dynamics has been the primary theoretical tool for probing such mechanisms at the atomistic scale. However, the complexity of the interface, requiring large simulation boxes, and the long time scales associated with dynamical processes pose substantial theoretical challenges for any method relying on explicit electronic structure calculations. High-Dimensional Neural Network Potentials (HDNNPs) offer a solution to these challenges, enabling atomistic simulations with DFT-level accuracy at only a fraction of the computational expense. Here, we present HDNNP-based simulations of ZnO-water interfaces, providing insights into their structure and dynamics.

Keywords: Solid-liquid interfaces; Machine learning potentials