Data‐Driven Interpretable Descriptors for the Structure–Activity Relationship of Surface Lattice Oxygen on Doped Vanadium Oxides
Chenggong Jiang, Hongbo Song, Guodong Sun, Xin Chang, Shiyu Zhen, Shican Wu, Zhi‐Jian Zhao, Jinlong Gong
Abstract
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.