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Deeper learning in electrocatalysis: realizing opportunities and addressing challenges
Journal article   Open access

Deeper learning in electrocatalysis: realizing opportunities and addressing challenges

John A Keith, James R McKone, Joshua D Snyder and Maureen H Tang
Current opinion in chemical engineering, v 36, 100824
Jun 2022
DOI: https://doi.org/10.1016/j.coche.2022.100824
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.1016/j.coche.2022.100824View
Published, Version of Record (VoR)CC BY V4.0,  Open

Abstract

Emerging techniques in deep learning have created exciting opportunities for next-generation electrochemical technologies. While deep learning has been revolutionizing many research fields, strategies for its implementation for electrocatalysis remain nascent. This Opinion calls on the electrocatalysis community to join together and introduce a paradigm shift by establishing standards for reporting and sharing data from electrocatalysis investigations. We speculate on a possible future where crowd-sourced and standardized data from experimental and computational researchers can be analyzed collectively to better understand fundamental electrochemistry, yielding unprecedented insights for the development of new electrocatalysts. We identify key barriers to realizing this opportunity and how they might be overcome.

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15 Record Views
10 citations in Web of Science
10 citations in Scopus

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Collaboration types
Domestic collaboration
Web of Science research areas
Biotechnology & Applied Microbiology
Engineering, Chemical
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