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Active learning guides discovery of a champion four-metal perovskite oxide for oxygen evolution electrocatalysis

Cited 8 time in Web of Science Cited 10 time in Scopus
Authors

Moon, Junseok; Beker, Wiktor; Siek, Marta; Kim, Jiheon; Lee, Hyeon Seok; Hyeon, Taeghwan; Grzybowski, Bartosz A.

Issue Date
2023-11
Publisher
Nature Publishing Group
Citation
Nature Materials, Vol.23 No.1, pp.108-115
Abstract
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.
ISSN
1476-1122
URI
https://hdl.handle.net/10371/198919
DOI
https://doi.org/10.1038/s41563-023-01707-w
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  • College of Engineering
  • School of Chemical and Biological Engineering
Research Area Chemistry, Materials Science

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