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Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts

Author

Listed:
  • Baicheng Weng

    (The University of Toledo
    Soochow University
    Central South University)

  • Zhilong Song

    (Soochow University)

  • Rilong Zhu

    (Hunan University)

  • Qingyu Yan

    (Hunan University)

  • Qingde Sun

    (Soochow University)

  • Corey G. Grice

    (The University of Toledo)

  • Yanfa Yan

    (The University of Toledo)

  • Wan-Jian Yin

    (Soochow University
    Soochow University)

Abstract

Symbolic regression (SR) is an approach of interpretable machine learning for building mathematical formulas that best fit certain datasets. In this work, SR is used to guide the design of new oxide perovskite catalysts with improved oxygen evolution reaction (OER) activities. A simple descriptor, μ/t, where μ and t are the octahedral and tolerance factors, respectively, is identified, which accelerates the discovery of a series of new oxide perovskite catalysts with improved OER activity. We successfully synthesise five new oxide perovskites and characterise their OER activities. Remarkably, four of them, Cs0.4La0.6Mn0.25Co0.75O3, Cs0.3La0.7NiO3, SrNi0.75Co0.25O3, and Sr0.25Ba0.75NiO3, are among the oxide perovskite catalysts with the highest intrinsic activities. Our results demonstrate the potential of SR for accelerating the data-driven design and discovery of new materials with improved properties.

Suggested Citation

  • Baicheng Weng & Zhilong Song & Rilong Zhu & Qingyu Yan & Qingde Sun & Corey G. Grice & Yanfa Yan & Wan-Jian Yin, 2020. "Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-17263-9
    DOI: 10.1038/s41467-020-17263-9
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    Cited by:

    1. Xiaoyun Lin & Xiaowei Du & Shican Wu & Shiyu Zhen & Wei Liu & Chunlei Pei & Peng Zhang & Zhi-Jian Zhao & Jinlong Gong, 2024. "Machine learning-assisted dual-atom sites design with interpretable descriptors unifying electrocatalytic reactions," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    2. Zhilong Song & Linfeng Fan & Shuaihua Lu & Chongyi Ling & Qionghua Zhou & Jinlan Wang, 2025. "Inverse design of promising electrocatalysts for CO2 reduction via generative models and bird swarm algorithm," Nature Communications, Nature, vol. 16(1), pages 1-10, December.
    3. Yao, Qiuxiang & Wang, Linyang & Ma, Mingming & Ma, Li & He, Lei & Ma, Duo & Sun, Ming, 2024. "A quantitative investigation on pyrolysis behaviors of metal ion-exchanged coal macerals by interpretable machine learning algorithms," Energy, Elsevier, vol. 300(C).
    4. Xiaoxin Zhang & Hongyuan He & Yu Chen & Guangming Yang & Xiao Xiao & Haiping Lv & Yongkang Xiang & Shuxiong Wang & Chang Jiang & Jianhui Li & Zhou Chen & Subiao Liu & Ning Yan & Xue Yong & Abdullah N., 2025. "Co-expression of multi-genes for polynary perovskite electrocatalysts for reversible solid oxide cells," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
    5. Qian-Cheng Zhao & Lin Chen & Sicong Ma & Zhi-Pan Liu, 2025. "Data-driven discovery of Pt single atom embedded germanosilicate MFI zeolite catalysts for propane dehydrogenation," Nature Communications, Nature, vol. 16(1), pages 1-10, December.

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