Issue 9, 2023

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

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.

Graphical abstract: 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

Supplementary files

Article information

Article type
Communication
Submitted
31 Cax 2023
Accepted
22 Qad 2023
First published
24 Qad 2023
This article is Open Access
Creative Commons BY license

Energy Adv., 2023,2, 1351-1356

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

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