Regensburg 2025 – scientific programme
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O: Fachverband Oberflächenphysik
O 34: Poster Solid-Liquid Interfaces: Reactions and Electrochemistry
O 34.3: Poster
Tuesday, March 18, 2025, 13:30–15:30, P3
Exploration of the Pt(111)-water interface by high-dimensional neural network potentials — •Daniel Trzewik1,2, Moritz R. Schäfer1,2, Alexander L. Knoll1,2, and Jörg Behler1,2 — 1Theoretische Chemie II, Ruhr-Universität Bochum, Germany — 2Research Center Chemical Sciences and Sustainability, Research Alliance Ruhr, Germany
Detailed insights into solid-liquid interfaces are crucial for understanding many processes in catalysis and electrochemistry. Accurately modeling these interfaces using first-principles methods is computationally very demanding, which strongly restricts the complexity of the systems that can be studied. Machine learning potentials now can provide an efficient alternative with almost no loss in accuracy. In this study, high-dimensional neural network potentials (HDNNPs) are employed to investigate the Pt(111)-water interface in detail. After training to DFT reference data, molecular dynamics simulations are utilized to uncover the structural and dynamical properties of the interfacial water molecules.
Keywords: Molecular Dynamics; Machine Learning Potentials; Interfaces; Water