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

被引：6

作者：

Sugawara, Yuuki ^{[1
]}

Chen, Xiao ^{[2
]}

Higuchi, Ryusei ^{[3
]}

Yamaguchi, Takeo ^{[1
]}

机构：

[1] Tokyo Inst Technol, Lab Chem & Life Sci, Inst Innovat Res, R1 17,4259 Nagatsuta, Yokohama, Kanagawa 2268503, Japan

[2] Tokyo Inst Technol, Sch Engn, 4259 Nagatsuta, Yokohama, Kanagawa 2268503, Japan

[3] Tokyo Inst Technol, Inst Innovat Res, Lab Mat & Struct, 4259 Nagatsuta, Yokohama 2268503, Japan

来源：

ENERGY ADVANCES | 2023年 / 2卷 / 09期

关键词：

PEROVSKITES; CATALYST;

D O I：

10.1039/d3ya00238a

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

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. Machine learning analysis revealed the importance of structural features involving A-site metals in AxByOz multimetal oxides for their OER activity.

引用

页码：1351 / 1356

页数：7

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