Prediction of perovskite oxygen vacancies for oxygen electrocatalysis at different temperatures

Zhiheng Li, Xin Mao, Desheng Feng, Mengran Li (), Xiaoyong Xu (), Yadan Luo (), Linzhou Zhuang, Rijia Lin, Tianjiu Zhu, Fengli Liang, Zi Huang, Dong Liu, Zifeng Yan, Aijun Du, Zongping Shao () and Zhonghua Zhu ()
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Zhiheng Li: The University of Queensland
Xin Mao: Queensland University of Technology
Desheng Feng: The University of Queensland
Mengran Li: The University of Melbourne
Xiaoyong Xu: The University of Queensland
Yadan Luo: The University of Queensland
Linzhou Zhuang: East China University of Science and Technology
Rijia Lin: The University of Queensland
Tianjiu Zhu: The University of Queensland
Fengli Liang: The University of Queensland
Zi Huang: The University of Queensland
Dong Liu: China University of Petroleum
Zifeng Yan: China University of Petroleum
Aijun Du: Queensland University of Technology
Zongping Shao: Curtin University
Zhonghua Zhu: The University of Queensland

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: Abstract Efficient catalysts are imperative to accelerate the slow oxygen reaction kinetics for the development of emerging electrochemical energy systems ranging from room-temperature alkaline water electrolysis to high-temperature ceramic fuel cells. In this work, we reveal the role of cationic inductive interactions in predetermining the oxygen vacancy concentrations of 235 cobalt-based and 200 iron-based perovskite catalysts at different temperatures, and this trend can be well predicted from machine learning techniques based on the cationic lattice environment, requiring no heavy computational and experimental inputs. Our results further show that the catalytic activity of the perovskites is strongly correlated with their oxygen vacancy concentration and operating temperatures. We then provide a machine learning-guided route for developing oxygen electrocatalysts suitable for operation at different temperatures with time efficiency and good prediction accuracy.

Date: 2024
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DOI: 10.1038/s41467-024-53578-7

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