SKM 2023 – scientific programme
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O: Fachverband Oberflächenphysik
O 78: Heterogeneous Catalysis and Surface Dynamics II
O 78.5: Talk
Thursday, March 30, 2023, 11:30–11:45, TRE Phy
Machine Learning Driven Molecular Dynamics Simulation of the M1 Selective Oxidation Catalysts — •Kyeonghyeon Nam1, Yonghyuk Lee1, Liudmyla Masliuk2, Thomas Lunkenbein2, Annette Trunschke2, Christoph Scheurer1, and Karsten Reuter1 — 1Theory, Fritz-Haber-Institut der MPG — 2Inorganic Chem. Dept., Fritz-Haber-Institut der MPG, Berlin, Germany
The activity and selectivity of realistic heterogeneous catalysts can be altered noticeably by small changes in a multitude of factors such as bulk composition, dopants, defects, reaction conditions, etc. Their effects are furthermore interrelated in non-trivial ways. As an important first step to rationally disentangle them, we here aim to understand their influences on the evolution of local atomic-scale structural motifs presented by the catalyst. Specifically, we do this for the M1 structural modification of (Mo,V)Ox and (Mo,V,Te,Nb)Ox as an active catalyst for oxidative dehydrogenation of ethane to ethylene. The large primitive cell of the M1 catalyst challenges a detailed study of all surface terminations by means of predictive-quality first-principles calculations. To this end, we deconstruct the primitive cell into ‘rod-like structures’ of surface motifs with various oxygen content. A machine-learned Gaussian Approximation Potential (GAP), trained against this structural library, faithfully reproduces experimental data from electron microscopy [1]. MD simulations of M1 catalyst hk0 prismatic faces with the iteratively improved GAP help to rationalize the influence of vanadium and niobium doping on the active surface structure.
[1] L. Masliuk et al., J. Phys. Chem. C 121, 24093 (2017).