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Catalyst design with machine learning

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.


  1. Carneiro, J. & Nikolla, E. Nano Res. 12, 2081–2092 (2019).

    Article  Google Scholar 

  2. Zhai, S. et al. Nat. Energy (2022).

    Article  Google Scholar 

  3. Li, Z., Achenie, L. E. K. & Xin, H. ACS Catal. 10, 4377–4384 (2020).

    Article  Google Scholar 

  4. Weng, B. et al. Nat. Commun. 11, 1–8 (2020).

    Article  Google Scholar 

  5. Suntivich, J. et al. Nat. Chem. 3, 546–550 (2011).

    Article  Google Scholar 

  6. Wang, X. et al. Nat. Commun. 10, 704 (2019).

    Article  Google Scholar 

  7. Jacobs, R., Hwang, J., Shao-Horn, Y. & Morgan, D. Chem. Mater. 31, 785–797 (2019).

    Article  Google Scholar 

  8. Esterhuizen, J. A., Goldsmith, B. R. & Linic, S. Nat. Catal. 5, 175–184 (2022).

    Article  Google Scholar 

  9. Wang, S.-H., Pillai, H. S., Wang, S., Achenie, L. E. K. & Xin, H. Nat. Commun. 12, 5288 (2021).

    Article  Google Scholar 

  10. Kolluru, A. et al. ACS Catal. 12, 8572–8581 (2022).

    Article  Google Scholar 

Download references

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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).

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