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