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A combined ionic Lewis acid descriptor and machine-learning approach to prediction of efficient oxygen reduction electrodes for ceramic fuel cells

Nature Energy volume 7, pages 866–875 (2022)Cite this article

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Abstract

Improved, highly active cathode materials are needed to promote the commercialization of ceramic fuel cell technology. However, the conventional trial-and-error process of material design, characterization and testing can make for a long and complex research cycle. Here we demonstrate an experimentally validated machine-learning-driven approach to accelerate the discovery of efficient oxygen reduction electrodes, where the ionic Lewis acid strength (ISA) is introduced as an effective physical descriptor for the oxygen reduction reaction activity of perovskite oxides. Four oxides, screened from 6,871 distinct perovskite compositions, are successfully synthesized and confirmed to have superior activity metrics. Experimental characterization reveals that decreased A-site and increased B-site ISAs in perovskite oxides considerably improve the surface exchange kinetics. Theoretical calculations indicate such improved activity is mainly attributed to the shift of electron pairs caused by polarization distribution of ISAs at sites A and B, which greatly reduces oxygen vacancy formation energy and migration barrier.

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Fig. 1: The overall workflow diagram.
Fig. 2: Model evaluation and descriptor importance degree analysis.
Fig. 3: Structure and electrochemical performance of the synthesized perovskite oxide sample.
Fig. 4: Symmetrical cell stability and single cell performance based on SCCN cathode.
Fig. 5: Morphology of BSCCFM.
Fig. 6: Oxygen-transfer-related characterization.
Fig. 7: DFT calculation of electronic structure evolution.

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Data availability

All relevant data are included in the paper and its Supplementary Information. Source Data are provided with this paper.

Code availability

The Python script for machine-learning model training and material screening are available at https://github.com/nlpcui/MLORR.

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Acknowledgements

This work is supported by National Natural Science Foundation of China (grant no. 51827901, H.X.), Project of Strategic Importance Program of The Hong Kong Polytechnic University (grant no P0035168, M.N.), Sichuan Science and Technology Department (grant no. 2020YFH0012, H.X.), Program for Guangdong Introducing Innovative and Entrepreneurial Teams (grant no. 2019ZT08G315, H.X.), as well as the Natural Science Foundation of Guangdong Province (grant no. 2020A1515010550, H.X.).

Author information

Authors and Affiliations

  1. Guangdong Provincial Key Laboratory of Deep Earth Sciences and Geothermal Energy Exploitation and Utilization, Institute of Deep Earth Sciences and Green Energy, Shenzhen University, Shenzhen, China

    Shuo Zhai, Heping Xie & Bin Chen

  2. Department of Building and Real Estate, Research Institute for Sustainable Urban Development (RISUD) & Research Institute for Smart Energy (RISE), The Hong Kong Polytechnic University, Kowloon, China

    Shuo Zhai, Daqin Guan, Jian Wang, Siyuan Zhao & Meng Ni

  3. Institute of New Energy and Low-Carbon Technology, Sichuan University, Chengdu, China

    Shuo Zhai & Heping Xie

  4. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

    Peng Cui

  5. State Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, Nanjing, China

    Daqin Guan, Yufei Song & Zongping Shao

  6. WA School of Mines: Minerals, Energy and Chemical Engineering (WASM-MECE), Curtin University, Perth, Western Australia, Australia

    Zongping Shao

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Contributions

S.Z., H.X., M.N. and Z.S. conceived the idea and designed the experiments. P.C. provided machine-learning codes. S.Z., S.Y.Z. and B.C. carried out the sample synthesis, characterization and measurements. Y.S, D.G. and J.W. participated in the discussion of the experimental results. S.Z., H.X., M.N. and Z.S. co-wrote and revised the manuscript.

Corresponding authors

Correspondence to Heping Xie, Zongping Shao or Meng Ni.

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The authors declare no competing interests.

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Nature Energy thanks Yueh-Lin Lee, Steven McIntosh 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, Notes 1–5, Figs. 1–24 and Tables 1–8.

Supplementary Data 1

The nine ionic descriptor values for each perovskite in the dataset.

Supplementary Data 2

The ASR predicted values for 6,871 distinct perovskite oxide compositions.

Source data

Source Data Fig. 2

Model evaluation and descriptor importance degree analysis.

Source Data Fig. 3

Structure and electrochemical performance of the synthesized perovskite oxide sample.

Source Data Fig. 4

Symmetrical cell stability and single cell performance based on SCCN cathode.

Source Data Fig. 6

Oxygen-transfer related characterization.

Source Data Fig. 7

DFT calculation of electronic structure evolution.

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Zhai, S., Xie, H., Cui, P. et al. A combined ionic Lewis acid descriptor and machine-learning approach to prediction of efficient oxygen reduction electrodes for ceramic fuel cells. Nat Energy 7, 866–875 (2022). https://doi.org/10.1038/s41560-022-01098-3

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