Identifying Stable Electrocatalysts Initialized by Data Mining: Sb WO for Oxygen Reduction.

Data mining from computational materials database has become a popular strategy to identify unexplored catalysts. Herein, the opportunities and challenges of this strategy are analyzed by investigating a discrepancy between data mining and experiments in identifying low-cost metal oxide (MO) electrocatalysts. Based on a search engine capable of identifying stable MOs at the pH and potentials of interest, a series of MO electrocatalysts is identified as potential candidates for various reactions. Sb WO attracted the attention among the identified stable MOs in acid. Based on the aqueous stability diagram, Sb WO is stable under oxygen reduction reaction (ORR) in acidic media but rather unstable under high-pH ORR conditions. However, this contradicts to the subsequent experimental observation in alkaline ORR conditions. Based on the post-catalysis characterizations, surface state analysis, and an advanced pH-field coupled microkinetic modeling, it is found that the Sb WO surface will undergo electrochemical passivation under ORR potentials and form a stable and 4e-ORR active surface. The results presented here suggest that though data mining is promising for exploring electrocatalysts, a refined strategy needs to be further developed by considering the electrochemistry-induced surface stability and activity.