Berlin 2024 – scientific programme
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O: Fachverband Oberflächenphysik
O 32: Poster: Solid-Liquid Interfaces
O 32.3: Poster
Tuesday, March 19, 2024, 18:00–20:00, Poster C
Development of high-dimensional neural network potentials for solid-liquid interfaces — •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
Solid-liquid interfaces play an essential role for chemical processes involving catalysis, electrochemistry and materials science. Modelling of these interfaces with first-principles methods remains computationally demanding due to the required system size. Machine learning potentials offer an efficient alternative at similar level of accuracy. The utilized high-dimensional neural network potentials (HDNNPs) in this project allow for a detailed investigation of solid-water interfaces. Molecular dynamics simulations reveal the structural arrangement and properties of the interface water as well as the interaction with the surface.