Machine learning in fundamental electrochemistry: Recent advances and future opportunities

被引：16

作者：

Chen, Haotian ^{[1
]}

Kaetelhoen, Enno ^{[2
]}

Compton, Richard G. ^{[1
]}

机构：

[1] Univ Oxford, Dept Chem, Phys & Theoret Chem Lab, South Parks Rd, Oxford OX1 3QZ, England

[2] Accenture GmbH, Campus Kronberg, D-61476 Kronberg, Germany

来源：

CURRENT OPINION IN ELECTROCHEMISTRY | 2023年 / 38卷

关键词：

UNIVERSAL APPROXIMATION; NONLINEAR OPERATORS; NEURAL-NETWORKS; VOLTAMMETRY; INFERENCE;

D O I：

10.1016/j.coelec.2023.101214

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：

The last decade has seen a rapid increase in the use of ma-chine learning techniques in an ever-broadening range of ap-plications. Despite great opportunities, its benefits have, however, not yet been exploited to the full extent in the field of electrochemistry. This paper briefly reviews recent activities at the interface of machine learning and electrochemistry, dis-cusses the challenges researchers have encountered, and points out opportunities for future research and application.

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页数：9

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