Dresden 2026 – wissenschaftliches Programm
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O: Fachverband Oberflächenphysik
O 24: Solid-liquid interfaces: Reactions and electrochemistry – Poster
O 24.6: Poster
Montag, 9. März 2026, 18:00–20:00, P2
Developing High-Dimensional Neural Network Potentials for Studying Tricalcium Silicate-Water Interfaces — •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 emergence of machine learning potentials (MLP) trained on 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 several solid minerals. In this study, we investigate interfaces of liquid water with alite (Ca3SiO5), a calcium silicate forming several polymorphs, which is characterized by a particularly high reactivity with water. Insights into the behavior of water at calcium silicate surfaces at an atomistic level are crucial to reach a better understanding of general hydration reactions of this class of materials. An approach for realizing high-accuracy, large-scale MD simulations is presented.
Keywords: Machine learning; Molecular Dynamics; water interfaces; non-ideal surfaces