Regensburg 2025 – wissenschaftliches Programm
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O: Fachverband Oberflächenphysik
O 34: Poster Solid-Liquid Interfaces: Reactions and Electrochemistry
O 34.4: Poster
Dienstag, 18. März 2025, 13:30–15:30, P3
Studying Tricalcium Silicate-Water Interfaces Using High-Dimensional Neural Network Potentials — •Henry Wang1,2, Bernadeta Prus1,2, and Jörg Behler1,2 — 1Theoretische Chemie II, Ruhr-Universität Bochum, Germany — 2Research Center Chemical Sciences and Sustainability, Research Alliance Ruhr, Germany
The advent of machine learning potentials (MLP) trained to energies and forces from electronic structure calculations has revolutionized the simulation of solid-liquid interfaces by molecular dynamics (MD). For instance, High-Dimensional Neural Network Potentials (HDNNP) have shown excellent accuracy for describing the interaction of water with numerous solid minerals. In this study, we investigate interfaces of liquid water with alite (Ca3SiO5), an important cement mineral exhibiting various polymorphic states. Using large-scale MD simulations, an analysis of the structural and dynamical properties of interfacial water is presented.
Keywords: Machine learning; Molecular Dynamics; cement minerals; water interfaces