Multi-metal oxides in general and perovskite oxides in particular have attracted considerable attention as oxygen evolution electrocatalysts. Although numerous theoretical studies have been undertaken, the most promising perovskite-based catalysts continue to emerge from human-driven experimental campaigns rather than data-driven machine learning protocols, which are often limited by the scarcity of experimental data on which to train the models. This work promises to break this impasse by demonstrating that active learning on even small datasets—but supplemented by informative structural-characterization data and coupled with closed-loop experimentation—can yield materials of outstanding performance. The model we develop not only reproduces several non-obvious and actively studied experimental trends but also identifies a composition of a perovskite oxide electrocatalyst exhibiting an intrinsic overpotential at 10 mA cm–2oxide of 391 mV, which is among the lowest known of four-metal perovskite oxides.
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All data underlying this work are included in the main text and the Supplementary Information.
The comprehensive details concerning the codes used in this study to execute the ML-based protocols can be accessed at the GitHub repository: https://github.com/JunseokMoon/OER2022.
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This research was supported by the Institute for Basic Science, Korea (project codes IBS-R006-D1 to T.H. and IBS-R020-D1 to B.A.G.) as well as by the Allchemy, Inc. funds (to W.B.). We thank the Korea Basic Science Institute (KBSi) at Busan Center for XPS measurements, the National Instrumentation Center for Environmental Management (NICEM) at Seoul National University for ICP-AES analysis and the National Center for Inter-university Research Facilities (NCIRF) at Seoul National University for high-resolution TEM analysis.
The authors declare no competing interests.
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Moon, J., Beker, W., Siek, M. et al. Active learning guides discovery of a champion four-metal perovskite oxide for oxygen evolution electrocatalysis. Nat. Mater. (2023). https://doi.org/10.1038/s41563-023-01707-w