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The discovery and design of new materials which can efficiently catalyze theoxygen reduction and evolution reactions at reduced temperatures is importantfor facilitating the widespread adoption of fuel cell and electrolyzertechnologies. Numerous studies have produced correlations between catalyticproperties, such as oxygen surface exchange or electrode area specificresistance (ASR), and properties of the catalyst material. However,correlations have historically been limited in scope (e.g., using only a fewmaterials or at a single temperature) and it has been difficult to providedetailed assessments of their robustness. Here, we assess the ability of the Op-band center electronic structure descriptor, obtained from density functionaltheory (DFT) calculations, to correlate with oxygen surface exchange rates,diffusivities, and area specific resistances for a large database of perovskiteoxide catalytic properties. By data mining the literature, we obtain 747catalytic property value data points spanning 299 unique perovskitecompositions from 313 studies. We assess linear correlations of each propertywith the O p-band center and find generally modest correlations that arequalitatively useful (prediction mean absolute errors of about 0.5 log unitsare typical), where the correlations are improved at higher temperatures (e.g.,800 {\deg}C vs. 500 {\deg}C) and significantly improve when considering fits tothe subset of materials which have multiple independent measurements. Thesefindings suggest that the spread of property data is significantly influencedby experimental uncertainty, and subsequent measurements of additionalmaterials will likely improve the O p-band center correlations.

A Critical Assessment of Electronic Structure Descriptors for Predicting Perovskite Catalytic Properties