A Critical Assessment of Electronic Structure Descriptors for Predicting Perovskite Catalytic Properties
Ryan Jacobs, Jian Liu, Harry Abernathy, Dane Morgan

TL;DR
This study evaluates the effectiveness of the O p-band center electronic structure descriptor from DFT calculations in predicting catalytic properties of perovskite oxides, revealing modest correlations influenced by experimental uncertainties.
Contribution
It provides a large-scale assessment of the O p-band center descriptor's robustness across diverse perovskite materials and experimental conditions.
Findings
Correlations are modest but qualitatively useful.
Higher temperature measurements improve correlation.
Multiple measurements per material enhance predictive accuracy.
Abstract
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…
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Taxonomy
TopicsElectrocatalysts for Energy Conversion · Machine Learning in Materials Science · Perovskite Materials and Applications
