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

Gang Wang, Shinya Mine, Duotian Chen, Yuan Jing, Kah Wei Ting, Taichi Yamaguchi, Motoshi Takao, Zen Maeno, Ichigaku Takigawa (), Koichi Matsushita, Ken-ichi Shimizu () and Takashi Toyao ()
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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
Koichi Matsushita: ENEOS Corporation
Ken-ichi Shimizu: Hokkaido University
Takashi Toyao: Hokkaido University

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-41341-3

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