Improved, highly active cathode materials are needed to promote the commercialization of ceramic fuel cell technology. However, the conventional trial-and-error process of material design, characterization and testing can make for a long and complex research cycle. Here we demonstrate an experimentally validated machine-learning-driven approach to accelerate the discovery of efficient oxygen reduction electrodes, where the ionic Lewis acid strength (ISA) is introduced as an effective physical descriptor for the oxygen reduction reaction activity of perovskite oxides. Four oxides, screened from 6,871 distinct perovskite compositions, are successfully synthesized and confirmed to have superior activity metrics. Experimental characterization reveals that decreased A-site and increased B-site ISAs in perovskite oxides considerably improve the surface exchange kinetics. Theoretical calculations indicate such improved activity is mainly attributed to the shift of electron pairs caused by polarization distribution of ISAs at sites A and B, which greatly reduces oxygen vacancy formation energy and migration barrier.
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The Python script for machine-learning model training and material screening are available at https://github.com/nlpcui/MLORR.
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This work is supported by National Natural Science Foundation of China (grant no. 51827901, H.X.), Project of Strategic Importance Program of The Hong Kong Polytechnic University (grant no P0035168, M.N.), Sichuan Science and Technology Department (grant no. 2020YFH0012, H.X.), Program for Guangdong Introducing Innovative and Entrepreneurial Teams (grant no. 2019ZT08G315, H.X.), as well as the Natural Science Foundation of Guangdong Province (grant no. 2020A1515010550, H.X.).
The authors declare no competing interests.
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Model evaluation and descriptor importance degree analysis.
Structure and electrochemical performance of the synthesized perovskite oxide sample.
Symmetrical cell stability and single cell performance based on SCCN cathode.
Oxygen-transfer related characterization.
DFT calculation of electronic structure evolution.
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Zhai, S., Xie, H., Cui, P. et al. A combined ionic Lewis acid descriptor and machine-learning approach to prediction of efficient oxygen reduction electrodes for ceramic fuel cells. Nat Energy 7, 866–875 (2022). https://doi.org/10.1038/s41560-022-01098-3
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