Identifying Stable Electrocatalysts Initialized by Data Mining: Sb₂WO₆ for Oxygen Reduction

Abstract

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. Sb2WO6 attracted the attention among the identified stable MOs in acid. Based on the aqueous stability diagram, Sb2WO6 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 Sb2WO6 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.

Reversible Hydrogen Electrode (RHE) Scale Dependent Surface Pourbaix Diagram at Different pH

“…A HO*/O* precovered surface may form a passivation layer, which subsequently determines the actual stability of an electrocatalyst under operating conditions. 16,18,19 Therefore, dismissing the realistic surface coverage of an electrocatalyst will likely lead to high uncertainty in predicting the electrocatalytic activity of a catalyst surface.…”

“… Some surface states (e.g., surfaces with the formation of many anion vacancies) will lead to surface reconstruction A HO*/O* precovered surface may form a passivation layer, which subsequently determines the actual stability of an electrocatalyst under operating conditions. ,, …”

“…A HO*/O* precovered surface may form a passivation layer, which subsequently determines the actual stability of an electrocatalyst under operating conditions. ,, …”

Accelerating materials discovery for electrocatalytic water oxidation via center-environment deep learning in spinel oxides

“…Similarly, Moon et al 44 employed active learning to discover efficient perovskite oxide catalysts, demonstrating how ML can accelerate the discovery of catalysts. Xue et al 45,46 utilized machine learning techniques to discover promising ternary systems, such as Mn–Ca–La, Mn–Ca–Y, and Mn–Mg–Ca, for enhancing catalytic activities in hydrogen fuel cells, showcasing the potential of artificial intelligence in catalyst design and materials discovery. Meanwhile, another study delved into the challenges of data mining strategies, particularly in reconciling computational predictions with experimental observations, highlighting the importance of refining strategies for exploring new electrocatalysts under different reaction conditions.…”