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O: Fachverband Oberflächenphysik
O 24: Solid-liquid interfaces: Reactions and electrochemistry – Poster
O 24.7: Poster
Montag, 9. März 2026, 18:00–20:00, P2
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
In-depth knowledge of solid-liquid interfaces is essential for understanding numerous catalytic and electrochemical processes. However, accurately describing these interfaces with first-principles methods is computationally demanding, limiting the system sizes and complexities that can be explored. Modern machine learning potentials offer a powerful alternative, delivering near-first-principles accuracy at a fraction of the cost. In this work, we use high-dimensional neural network potentials (HDNNPs) to examine the Pt(111)-water interface in detail. Trained on DFT reference data, these models enable molecular dynamics simulations which reveal the structural and dynamical behavior of water molecules at the interface.
Keywords: Molecular Dynamics; Machine Learning Potentials; Interfaces; Water