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SOLID-OXIDE FUEL CELLS

Catalyst design with machine learning

Nature Energy volume 7, pages 790–791 (2022)Cite this article

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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.

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Fig. 1: Interpretable machine learning for discovery of catalytic materials for solid-oxide fuel cells.

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  1. Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA

    Hongliang Xin

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  1. Hongliang Xin
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Correspondence to Hongliang Xin.

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Xin, H. Catalyst design with machine learning. Nat Energy 7, 790–791 (2022). https://doi.org/10.1038/s41560-022-01112-8

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