Descriptors of Oxygen-Evolution Activity for Oxides: A Statistical Evaluation

Catalysts
for oxygen electrochemical processes are critical for
the commercial viability of renewable energy storage and conversion
devices such as fuel cells, artificial photosynthesis, and metal-air
batteries. Transition metal oxides are an excellent system for developing
scalable, non-noble-metal-based catalysts, especially for the oxygen
evolution reaction (OER). Central to the rational design of novel
catalysts is the development of quantitative structure–activity
relationships, which correlate the desired catalytic behavior to structural
and/or elemental descriptors of materials. The ultimate goal is to
use these relationships to guide materials design. In this study,
101 intrinsic OER activities of 51 perovskites were compiled from
five studies in literature and additional measurements made for this
work. We explored the behavior and performance of 14 descriptors of
the metal–oxygen bond strength using a number of statistical
approaches, including factor analysis and linear regression models.
We found that these descriptors can be classified into five descriptor
families and identify electron occupancy and metal–oxygen covalency
as the dominant influences on the OER activity. However, multiple
descriptors still need to be considered in order to develop strong
predictive relationships, largely outperforming the use of only one
or two descriptors (as conventionally done in the field). We confirmed
that the number of d electrons, charge-transfer energy (covalency),
and optimality of eg occupancy play the important roles,
but found that structural factors such as M–O–M bond
angle and tolerance factor are relevant as well. With these tools,
we demonstrate how statistical learning can be used to draw novel
physical insights and combined with data mining to rapidly screen
OER electrocatalysts across a wide chemical space.