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Data-driven Interpretable Descriptors for Structure-Activity Relation of Surface Lattice Oxygen on Doped Vanadium Oxides.

Understanding the structure-activity relation of surface lattice oxygen is critical but challenging to design efficient redox catalysts. This paper describes data-driven redox activity descriptors on doped vanadium oxides combining density functional theory and interpretable machine learning. We corroborate p-band center is the most crucial feature about the activity. Besides, some features from the coordination environment, including unoccupied d-band center, s- and d-band fillings, also play important roles in tuning oxygen activity. Further analysis reveals that data-driven descriptors could decode more information about electron transfer during the redox process. Based on descriptors, we report that atomic Re- and W-doping could inhibit over-oxidation in chemical looping oxidative dehydrogenation of propane, which is verified by subsequent experiments and calculations. This work shed light on the structure-activity relation of lattice oxygen to design redox catalysts rationally.