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The explosive increase in popularity and increased accessibility to artificial intelligence and data science tools have opened the door for exploring previously deemed “too large” experimental spaces efficiently in experimental chemistry. This is of special interest for the field of electrocatalysis, where the development of new materials has relied largely on trial-and-error approaches that are not time- and resource-efficient. By leveraging these approaches, we can more effectively find promising electrocatalyst compositions and synthesis conditions that result in lower overpotentials and higher electrocatalyst durability. In this Technical Note, we use oxygen evolution electrocatalysts as a model to highlight potential pitfalls in the use of data-driven strategies for electrocatalyst development, focusing on the important choice of optimization metrics, normalization of data, and common errors that may appear during these kinds of approaches.

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  This document is the Accepted Manuscript version of a Published Work that appeared in final form in ACS Electrochemistry, copyright © 2025 American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acselectrochem.5c00153.

Keyword: machine learning

Date published: 2025-09-04

Publisher: American Chemical Society (ACS)

Journal:

• ACS Electrochemistry vol. 1 issue. 9 p. 1871-1877

Funding:

• University of Cambridge
• Japan Science and Technology Agency JPMJAP2421
• Japan Science and Technology Agency JPMJGX23H2
• National Institute for Materials Science
• Churchill College, University of Cambridge

Manuscript type: Author's version (Accepted manuscript)

MDR DOI: https://doi.org/10.48505/nims.5849

First published URL: https://doi.org/10.1021/acselectrochem.5c00153

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Updated at: 2026-04-30 12:01:11 +0900

Published on MDR: 2026-05-23 08:31:12 +0900