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O: Fachverband Oberflächenphysik

O 96: Structure of Solid/Liquid Interfaces II

O 96.2: Vortrag

Freitag, 11. März 2016, 10:45–11:00, S052

A High-Dimensional Neural Network Potential for Water at Zinc Oxide: First Applications to Non-Polar Interfaces — •Vanessa Quaranta, Matti Hellström, and Jörg Behler — Lehrstuhl für Theoretische Chemie, Ruhr-Universität Bochum, Germany

Zinc oxide (ZnO) is an important material in surface science, which has many applications in different fields [1]. In most of these applications, water is ubiquitous playing a crucial role. To understand these processes at the atomic level, realistic structural models of water/ZnO interfaces are crucial. However, the required large systems often containing thousands of atoms dramatically limit the application of ab initio techniques. In recent years, artificial neural networks (NNs) have emerged as a powerful efficient method to provide accurate PESs for a variety of systems [2]. Here, we report first results for a DFT-based NN potential constructed for liquid water/ZnO interfaces. In particular, the structural and dynamical properties of interfacial water molecules interacting with non-polar ZnO surfaces will be presented.

[1] Ch. Wöll, Progr. Surf. Sci. 82, 55 (2007).

[2] J. Behler, Phys. Chem. Chem. Phys. 13, 17930 (2011).