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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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    1. Victor Fung & Guoxiang Hu & P. Ganesh & Bobby G. Sumpter, 2021. "Machine learned features from density of states for accurate adsorption energy prediction," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    2. Yifan Wang & Jake Kalscheur & Ya-Qiong Su & Emiel J. M. Hensen & Dionisios G. Vlachos, 2021. "Real-time dynamics and structures of supported subnanometer catalysts via multiscale simulations," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    3. Benjamin J. Shields & Jason Stevens & Jun Li & Marvin Parasram & Farhan Damani & Jesus I. Martinez Alvarado & Jacob M. Janey & Ryan P. Adams & Abigail G. Doyle, 2021. "Bayesian reaction optimization as a tool for chemical synthesis," Nature, Nature, vol. 590(7844), pages 89-96, February.
    4. Shih-Han Wang & Hemanth Somarajan Pillai & Siwen Wang & Luke E. K. Achenie & Hongliang Xin, 2021. "Infusing theory into deep learning for interpretable reactivity prediction," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    5. Zachary W. Ulissi & Andrew J. Medford & Thomas Bligaard & Jens K. Nørskov, 2017. "To address surface reaction network complexity using scaling relations machine learning and DFT calculations," Nature Communications, Nature, vol. 8(1), pages 1-7, April.
    6. Aliaksei Mazheika & Yang-Gang Wang & Rosendo Valero & Francesc Viñes & Francesc Illas & Luca M. Ghiringhelli & Sergey V. Levchenko & Matthias Scheffler, 2022. "Artificial-intelligence-driven discovery of catalyst genes with application to CO2 activation on semiconductor oxides," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    7. Roman Schmack & Alexandra Friedrich & Evgenii V. Kondratenko & Jörg Polte & Axel Werwatz & Ralph Kraehnert, 2019. "A meta-analysis of catalytic literature data reveals property-performance correlations for the OCM reaction," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    8. Keith T. Butler & Daniel W. Davies & Hugh Cartwright & Olexandr Isayev & Aron Walsh, 2018. "Machine learning for molecular and materials science," Nature, Nature, vol. 559(7715), pages 547-555, July.
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