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