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O: Fachverband Oberflächenphysik
O 97: Solid-liquid interfaces: Reactions and electrochemistry III
O 97.6: Vortrag
Freitag, 13. März 2026, 11:00–11:15, TRE/PHYS
Investigating the Role of Nuclear Quantum Effects at Zinc Oxide-Water Interfaces with High-Dimensional Neural Network Potentials — •Jan Elsner1,2 and Jörg Behler1,2 — 1Theoretische Chemie II, Ruhr-Universität Bochum, Germany — 2Research Center Chemical Sciences and Sustainability, Research Alliance Ruhr, Germany
Zinc oxide is a promising material for sustainable hydrogen production via catalytic water splitting. The interface of ZnO with water exhibits complex dynamical behaviour, including water dissociation and recombination, as well as long-range proton transport. Previous studies [1] have elucidated these mechanisms using High-Dimensional Neural Network Potentials (HDNNPs), which enable simulations at the system sizes and timescales needed for statistically converged interfacial properties. However, the validity of the classical approximation for atomic nuclei in such systems, particularly for describing interfacial proton transfer (PT), remains poorly understood. Here, we employ a new efficient parallel implementation of path-integral molecular dynamics with HDNNPs [2] to investigate the role of nuclear quantum effects (NQEs) at the ZnO-water interface. We show that NQEs significantly lower free energy barriers for PT, and discuss the resulting implications for the interfacial dynamics of the system.
[1] Quaranta V., Hellström M., Behler J., J. Phys. Chem. Lett. 2017, 8, 1476-1483
[2] Shiga M., Elsner J., Behler J., Thomsen B., J. Chem. Phys. 2025, 163, 134119
Keywords: Solid-liquid interfaces; Machine learning potentials; Nuclear Quantum Effects