Data-Driven Interpretable Descriptors for the Structure-Activity Relationship of Surface Lattice Oxygen on Doped...

Understanding the structure-activity relationship 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 that the p-band center is the most crucial feature for 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 the oxygen activity. Further analysis reveals that data-driven descriptors could decode more information about electron transfer during the redox process. Based on the descriptors, we report that atomic Re- and W-doping could inhibit over-oxidation in the chemical looping oxidative dehydrogenation of propane, which is verified by subsequent experiments and calculations. This work sheds light on the structure-activity relationship of lattice oxygen for the rational design of redox catalysts.