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Catalyst design with machine learning

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  • Hongliang Xin

    (Virginia Polytechnic Institute and State University)

Abstract

Development of oxygen reduction catalysts is of key importance to a range of energy technologies; however, the process has long relied on slow trial-and-error approaches. Now, accelerated discovery of perovskite oxides for use as air electrodes in solid-oxide fuel cells is achieved with machine learning.

Suggested Citation

  • Hongliang Xin, 2022. "Catalyst design with machine learning," Nature Energy, Nature, vol. 7(9), pages 790-791, September.
    • Handle: RePEc:nat:natene:v:7:y:2022:i:9:d:10.1038_s41560-022-01112-8
    DOI: 10.1038/s41560-022-01112-8
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    Cited by:

    1. Snehi Shrestha & Kieran James Barvenik & Tianle Chen & Haochen Yang & Yang Li & Meera Muthachi Kesavan & Joshua M. Little & Hayden C. Whitley & Zi Teng & Yaguang Luo & Eleonora Tubaldi & Po-Yen Chen, 2024. "Machine intelligence accelerated design of conductive MXene aerogels with programmable properties," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

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