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O: Fachverband Oberflächenphysik

O 35: Poster Solid-Liquid Interfaces: Structure

O 35.2: Poster

Dienstag, 18. März 2025, 13:30–15:30, P3

High-Dimensional Neural Network Potentials for Molecular Dynamics Simulations of Mineral-Water Interfaces — •Maite Böhm^{1, 2}, Bernadeta Prus^{1, 2}, and Jörg Behler^{1, 2} — ¹Theoretische Chemie II, Ruhr-Universität Bochum, Germany — ²Research Center Chemical Sciences and Sustainability, Research Alliance Ruhr, Germany

In recent years, High-Dimensional Neural Network Potentials (HDNNP), a frequently used type of machine learning potential, have become a popular tool for simulations of complex systems such as mineral-water interfaces. Here, we present a HDNNP trained to density functional theory energies and forces for tricalcium aluminate (Ca₃Al₂O₆, C₃A)-water interfaces, which are of high interest for concrete chemistry. After validation, the obtained HDNNP is applied in large-scale molecular dynamics simulations to unravel the interactions of water with this material by computing a series of structural and dynamical properties.

Keywords: Machine Learning Potentials; Neural Networks; interfaces; water; Molecular Dynamics