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Stable acidic oxygen-evolving catalyst discovery through mixed accelerations

Nature Catalysis volume 9, pages 28–36 (2026)Cite this article

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Abstract

Ruthenium oxides (RuOx) are promising alternatives to iridium catalysts for the oxygen-evolution reaction in proton-exchange membrane water electrolysis but lack stability in acid. Alloying with other elements can improve stability and performance but enlarges the search space. Material acceleration platforms combining high-throughput experiments with machine learning can accelerate catalyst discovery, yet predicting and co-optimizing synthesizability, activity and stability remain challenging. A predictive featurization workflow that links a hypothesized catalyst to its actual single- or mixed-phase synthesis and acidic oxygen-evolution reaction properties has not been reported. Here we report a hierarchical workflow, termed mixed acceleration, integrating theoretical and experimental descriptors to predict synthesis, activity and stability. Guided by mixed acceleration through 379 experiments, we identified seven ruthenium-based oxides surpassing the Pareto frontier of activity and stability. The most balanced composition, Ru0.5Zr0.1Zn0.4Ox, achieved an overpotential of 194 mV at 10 mA cm−2 with a ruthenium dissolution rate 12 times lower than that of RuO2.

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Fig. 1: MA workflow.
Fig. 2: Mixed-metal ruthenium oxide sol–gel synthesis.
Fig. 3: OER performance evaluation.
Fig. 4: OER stability predictions with MA.

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Data availability

The authors declare that all data supporting the findings of this study are available within the paper and the Supplementary Information files, or from the corresponding authors upon request. Data used in ML models are available from GitHub via https://github.com/kangming-li/Stable-OER-Catalyst-Discovery-Through-Mixed-Acceleration.

Code availability

The code used in this work is available from GitHub via https://github.com/kangming-li/Stable-OER-Catalyst-Discovery-Through-Mixed-Acceleration.

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Acknowledgements

The authors acknowledge support from the Alliance for AI-Accelerated Materials Discovery (A3MD), which includes funding from Total Energies SE, Meta and BP. This research was undertaken thanks in part to funding provided to the University of Toronto’s Acceleration Consortium from the Canada First Research Excellence Fund (grant number CFREF-2022-00042).

Author information

Author notes

  1. Kangming Li

    Present address: Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

  2. These authors contributed equally: Yang Bai, Kangming Li, Ning Han.

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada

    Yang Bai, Ning Han, Jiheon Kim, Yongxiang Liang, Sjoerd Hoogland, Jianan Erick Huang & Edward H. Sargent

  2. The Alliance for AI-Accelerated Materials Discovery (A3MD), University of Toronto, Toronto, Ontario, Canada

    Yang Bai, Ning Han, Jiheon Kim, Sjoerd Hoogland, David Sinton, Edward H. Sargent & Jason Hattrick-Simpers

  3. Acceleration Consortium, University of Toronto, Toronto, Ontario, Canada

    Yang Bai, Kangming Li, Ali Shayesteh Zeraati, Brandon R. Sutherland, Kelvin Chow, David Sinton & Jason Hattrick-Simpers

  4. Department of Materials Science and Engineering, University of Toronto, Toronto, Ontario, Canada

    Kangming Li, Runze Zhang, Suhas Mahesh & Jason Hattrick-Simpers

  5. Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada

    Jiheon Kim & David Sinton

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Contributions

J.H.-S. and E.H.S. supervised the project. Y.B. and K.L. conceived the project idea and workflow. Y.B. performed materials synthesis, OER performance measurements and characterization investigations. K.L. built the ML workflow and models. N.H. performed OER performance measurements and characterization investigations. J.K. performed XRF measurements. R.Z. performed characterization investigations. S.M. performed XRD measurements. A.S.Z. performed X-ray photoelectron spectroscopy measurements. Y.L. performed OER measurements. S.H., J.E.H. and D.S. provided suggestions and feedback on the materials synthesis and mechanistic investigations. Y.B., K.L., A.S.Z., B.R.S., K.C., E.H.S. and J.H.-S. wrote and edited the paper. All the authors contributed to the discussion of the results and the final paper preparation.

Corresponding authors

Correspondence to Edward H. Sargent or Jason Hattrick-Simpers.

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The authors declare no competing interests.

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Peer review information

Nature Catalysis thanks Milad Abolhasani, Olga Kasian and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Methods, Discussion, Figs. 1–49 and Tables 1 and 2.

Supplementary Data 1

This spreadsheet contains the synthesis parameters for all materials reported in the study, including precursor identities, precursor ratios, solvent compositions, calcination programs, reaction temperatures, reaction times, and other experimental conditions used in each synthesis.

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Bai, Y., Li, K., Han, N. et al. Stable acidic oxygen-evolving catalyst discovery through mixed accelerations. Nat Catal 9, 28–36 (2026). https://doi.org/10.1038/s41929-025-01463-x

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