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Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach

Author

Listed:
  • Gang Wang

    (Hokkaido University)

  • Shinya Mine

    (Hokkaido University)

  • Duotian Chen

    (Hokkaido University)

  • Yuan Jing

    (Hokkaido University)

  • Kah Wei Ting

    (Hokkaido University)

  • Taichi Yamaguchi

    (Hokkaido University)

  • Motoshi Takao

    (Hokkaido University)

  • Zen Maeno

    (Kogakuin University)

  • Ichigaku Takigawa

    (RIKEN Center for Advanced Intelligence Project
    Hokkaido University
    Kyoto University)

  • Koichi Matsushita

    (ENEOS Corporation)

  • Ken-ichi Shimizu

    (Hokkaido University)

  • Takashi Toyao

    (Hokkaido University)

Abstract

Designing novel catalysts is key to solving many energy and environmental challenges. Despite the promise that data science approaches, including machine learning (ML), can accelerate the development of catalysts, truly novel catalysts have rarely been discovered through ML approaches because of one of its most common limitations and criticisms—the assumed inability to extrapolate and identify extraordinary materials. Herein, we demonstrate an extrapolative ML approach to develop new multi-elemental reverse water-gas shift catalysts. Using 45 catalysts as the initial data points and performing 44 cycles of the closed loop discovery system (ML prediction + experiment), we experimentally tested a total of 300 catalysts and identified more than 100 catalysts with superior activity compared to those of the previously reported high-performance catalysts. The composition of the optimal catalyst discovered was Pt(3)/Rb(1)-Ba(1)-Mo(0.6)-Nb(0.2)/TiO2. Notably, niobium (Nb) was not included in the original dataset, and the catalyst composition identified was not predictable even by human experts.

Suggested Citation

  • Gang Wang & Shinya Mine & Duotian Chen & Yuan Jing & Kah Wei Ting & Taichi Yamaguchi & Motoshi Takao & Zen Maeno & Ichigaku Takigawa & Koichi Matsushita & Ken-ichi Shimizu & Takashi Toyao, 2023. "Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41341-3
    DOI: 10.1038/s41467-023-41341-3
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    References listed on IDEAS

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