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O: Fachverband Oberflächenphysik
O 55: Poster Session IV: Poster to Mini-Symposium: Machine learning applications in surface science II
O 55.1: Poster
Tuesday, March 2, 2021, 13:30–15:30, P
Gaussian Approximation Potentials for Surface Catalysis — •Sina Stocker1, 2, Gábor Csányi3, Karsten Reuter1,2, and Johannes T. Margraf1, 2 — 1Technische Universität München, Germany — 2Fritz Haber Institut der Max Planck Gesellschaft, Berlin, Germany — 3University of Cambridge, United Kingdom
Predictive-quality first-principles based microkinetic models are increasingly used to analyze (and subsequently optimize) reaction mechanisms in heterogeneous catalysis. In full rigor, such models require the knowledge of all possible elementary reaction steps and their corresponding reaction barriers. Unfortunately, for complex catalytic processes (such as the generation of ethanol from syngas) the number of possible steps is so large that an exhaustive first-principles calculation of all barriers becomes prohibitively expensive.
To overcome this limitation, we develop a machine learned (ML) interatomic potential to model syngas conversion on Rhodium. This ML potential can be used to determine adsorption energies, geometries and reaction barriers for a large number of adsorbates at a fraction of the computational cost of the underlying first-principles method. Specifically, we use the Gaussian Approximation Potential (GAP) framework and explore iterative training and active learning to minimize the number of reference calculations. Here, the particular challenge lies in selecting representative configurations that adequately characterize the reactivity of molecules on a surface. Different training approaches will be compared.