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

Nature Materials volume 23, pages 108–115 (2024)Cite this article

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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.

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Fig. 1: Overview of the algorithms used and the workflow of the closed-loop protocol.
Fig. 2: Importance of characterization data.
Fig. 3: Optimization of the model and predicted experimental trends.
Fig. 4: Structural and electrochemical characterization of the best-performing CPCF oxide.
Fig. 5: Electrocatalytic OER performance and kinetics of the champion catalyst.

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Data availability

All data underlying this work are included in the main text and the Supplementary Information.

Code availability

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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Acknowledgements

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.

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Authors and Affiliations

  1. Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul, Republic of Korea

    Junseok Moon, Jiheon Kim, Hyeon Seok Lee & Taeghwan Hyeon

  2. School of Chemical and Biological Engineering, and Institute of Chemical Processes, Seoul National University (SNU), Seoul, Republic of Korea

    Junseok Moon, Jiheon Kim, Hyeon Seok Lee & Taeghwan Hyeon

  3. Allchemy, Inc., Highland, IN, USA

    Wiktor Beker

  4. Institute of Organic Chemistry, Polish Academy of Science, Warsaw, Poland

    Wiktor Beker & Bartosz A. Grzybowski

  5. Center for Soft and Living Matter, Institute for Basic Science (IBS), Ulsan, Republic of Korea

    Marta Siek & Bartosz A. Grzybowski

  6. Department of Chemistry, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea

    Bartosz A. Grzybowski

Authors
  1. Junseok Moon
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Contributions

J.M. designed the workflow and algorithm, collected and analysed data and performed the materials characterization and electrochemical measurements. W.B. supervised the ML and data analysis part of the study and reviewed the algorithm. M.S. participated in writing and technical editing of the manuscript and prepared figures. J.K. participated in algorithm design and supervised the materials characterization. H.S.L. supervised the electrochemical measurements. B.A.G. and T.H. conceived and supervised the research. J.M., B.A.G. and T.H. wrote the manuscript with help from the other authors.

Corresponding authors

Correspondence to Taeghwan Hyeon or Bartosz A. Grzybowski.

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Supplementary information

Supplementary Information

Supplementary Sections 1–9, Figs. 1–24, Tables 1–15 and references.

Supplementary Data 1

Supplementary dataset including the average predicted overpotential, prediction uncertainty, entropy and A-site electronegativity values for the 10,101 candidate materials.

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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. 23, 108–115 (2024). https://doi.org/10.1038/s41563-023-01707-w

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