Machine learning-aided unraveling of the importance of structural features for the electrocatalytic oxygen evolution reaction on multimetal oxides based on their A-site metal configurations

Yuuki Sugawara; Xiao Chen; Ryusei Higuchi; Takeo Yamaguchi

doi:10.1039/D3YA00238A

Machine learning-aided unraveling of the importance of structural features for the electrocatalytic oxygen evolution reaction on multimetal oxides based on their A-site metal configurations†

Yuuki Sugawara,

*^a Xiao Chen,^b Ryusei Higuchi^c and Takeo Yamaguchi*^a

Author affiliations

* Corresponding authors

^a Laboratory for Chemistry and Life Science, Institute of Innovative Research, Tokyo Institute of Technology, R1-17, 4259 Nagatsuta, Midori, Yokohama, Japan
E-mail: sugawara.y.aa@m.titech.ac.jp, yamag@res.titech.ac.jp

^b School of Engineering, Tokyo Institute of Technology, 4259 Nagatsuta, Midori, Yokohama, Kanagawa 226-8503, Japan

^c Laboratory for Materials and Structures, Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsuta, Midori, Yokohama 226-8503, Japan

Abstract

There is a need for comprehensive descriptors to develop prominent electrocatalysts for use in the oxygen evolution reaction (OER) for water splitting. Through machine learning analysis of the data obtained from multimetal oxides that contain A-site alkaline-/rare-earth and B-site transition metals, this study revealed that the OER activities depend on the A-site-related structures.

This article is part of the themed collections: Energy Advances: Highlight Japan & South Korea, SDG 7: Affordable and clean energy and Artificial Intelligence & Machine Learning in Energy Storage & Conversion

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DOI: https://doi.org/10.1039/D3YA00238A
Article type: Communication
Submitted: 31 May 2023
Accepted: 22 Jul 2023
First published: 24 Jul 2023
This article is Open Access

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Energy Adv., 2023,2, 1351-1356

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Machine learning-aided unraveling of the importance of structural features for the electrocatalytic oxygen evolution reaction on multimetal oxides based on their A-site metal configurations

Y. Sugawara, X. Chen, R. Higuchi and T. Yamaguchi, Energy Adv., 2023, 2, 1351 DOI: 10.1039/D3YA00238A

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

Energy Advances

Machine learning-aided unraveling of the importance of structural features for the electrocatalytic oxygen evolution reaction on multimetal oxides based on their A-site metal configurations†

Abstract

Supplementary files

Article information

Download Citation

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Machine learning-aided unraveling of the importance of structural features for the electrocatalytic oxygen evolution reaction on multimetal oxides based on their A-site metal configurations

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