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Machine learning in fundamental electrochemistry: Recent advances and future opportunities AITranslate

Oxford University;Accenture GmbH, Campus Kronberg; Oxford University
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Publisher: Elsevier
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Abstract AITranslate

The last decade has seen a rapid increase in the use of machine learning techniques in an ever-broadening range of applications. 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, discusses the challenges researchers have encountered, and points out opportunities for future research and application.

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DOI:https://doi.org/10.1016/j.coelec.2023.101214

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Citation Information:

The last decade has seen a rapid increase in the use of machine learning techniques in an ever-broadening range of applications. 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, discusses the challenges researchers have encountered, and points out opportunities for future research and application.

quote

GB/T 7714-2015 [1] Haotian Chen, Enno Kätelhön, Richard G. Compton. Current Opinion in Electrochemistry, 2023(38). DOI:10.1016/j.coelec.2023.101214.
MLA [1] Haotian Chen, et al., Current Opinion in Electrochemistry, no. 38, 2023, https://doi.org/10.1016/j.coelec.2023.101214.
APA [1] Haotian Chen, Enno Kätelhön, & Richard G. Compton. (2023). Current Opinion in Electrochemistry(38). https://doi.org/10.1016/j.coelec.2023.101214
IEEE [1] Haotian Chen, Enno Kätelhön, and Richard G. Compton, Current Opinion in Electrochemistry, no. 38, 2023, doi: 10.1016/j.coelec.2023.101214.