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

Baicheng Weng, Zhilong Song, Rilong Zhu, Qingyu Yan, Qingde Sun, Corey G. Grice, Yanfa Yan () and Wan-Jian Yin ()
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Baicheng Weng: The University of Toledo
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

Nature Communications, 2020, vol. 11, issue 1, 1-8

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-17263-9

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