Abstract
AbstractThe physical and chemical properties of inorganic materials significantly depend on their crystal structures. Therefore, precise design of structures can promote the development of highly active electrocatalysts. Due to the present environmental issues, it is desirable to establish a structural factor that regulates the anodic oxygen evolution reaction (OER) in water splitting. Herein, we demonstrate structural descriptors using nine kinds of unreported iron‐based multimetal oxides with complicated structures and 15 kinds of previously reported iron‐based oxides. Density functional theory simulations find that their OER activities cannot be explained by the electronic descriptor, i. e., charge‐transfer energy. In contrast, the OER activities exhibit clear correlations with structural descriptors. To objectively determine the most dominant structural descriptor for OER electrocatalysis on iron‐based oxides, data‐driven machine learning (ML) is exploited. Thus, ML analysis reveals that Fe−O bond length is the most dominant structural descriptor for electrocatalytic OER on iron‐based oxides.
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