SKM 2023 – wissenschaftliches Programm
Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe
O: Fachverband Oberflächenphysik
O 52: Heterogeneous Catalysis and Surface Dynamics I
O 52.8: Vortrag
Mittwoch, 29. März 2023, 12:30–12:45, TRE Phy
Estimating Free Energy Barriers for Heterogeneous Catalytic Reactions with Machine Learning Potentials and Umbrella Sampling — •Sina Stocker1, Gábor Csányi2, Karsten Reuter1, and Johannes T. Margraf1 — 1Fritz-Haber-Institut der MPG, Berlin, Germany — 2University 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 machine learned (ML) interatomic potentials to model the early steps of syngas conversion on Rhodium. These ML potentials can be used to determine free energy reaction barriers at a fraction of the computational cost of the underlying first-principles method. Specifically, we use here the Gaussian Approximation Potential (GAP) framework and explore iterative training in combination with umbrella sampling for the CHO decomposition as an example.