Dresden 2026 – scientific programme
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O: Fachverband Oberflächenphysik
O 39: Focus Session: Structure and Dynamics of Solvent at Electrochemical Interfaces I
O 39.5: Talk
Tuesday, March 10, 2026, 11:45–12:00, WILL/A317
Insights into the Structure and Dynamics of Co3O4–Water Interfaces Using a High-Dimensional Neural Network Potential — •Amir Omranpour1,2 and Jörg Behler1,2 — 1Theoretische Chemie II, Ruhr-Universität Bochum, Germany — 2Research Center Chemical Sciences and Sustainability, Research Alliance Ruhr, Germany
Co3O4 is an important catalyst for the oxygen evolution reaction and for the oxidation of organic molecules in the liquid phase. Therefore, understanding the Co3O4–water interface is essential for understanding the mechanistic aspects of those catalytic processes. The interactions at such complex interfaces, including hydrogen-bond fluctuations, hydroxylation, proton transfer, and solvent structuring, are inherently reactive atomistic processes that cannot be fully described by continuum or implicit-solvent models. On the other hand, ab initio molecular dynamics with explicit solvent remain restricted to only a few picoseconds and a few hundred atoms. In this work, we overcome these limitations by training a high-dimensional neural network potential (HDNNP) on density functional theory data, which enables us to greatly extend the accessible time and length scales. Using this HDNNP, we carry out simulations that uncover the structure, dynamics, and reactivity of Co3O4–water interfaces in detail. Our simulations show that different surface terminations give rise to significantly different hydration structures. They also reveal how each termination templates the interfacial water network, affects hydroxylation, and determines the degree of structural ordering at the interface.
Keywords: Machine Learning Potentials; HDNNP; Atomistic Simulation; Cobalt Oxide; Catalysis