Pitfalls in Artificial Intelligence Powered Discovery Due to Electrocatalyst Evaluation Methodologies

Abraham Castro Garcia; Ken Sakaushi

doi:10.1021/acselectrochem.5c00153

journal article May 23, 2025

Pitfalls in Artificial Intelligence Powered Discovery Due to Electrocatalyst Evaluation Methodologies

Abraham Castro Garcia Ken Sakaushi

ACS Electrochemistry Vol. 1 No. 9 pp. 1871-1877 · American Chemical Society (ACS)

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References

42

[1]

Machine learning for molecular and materials science

Keith T. Butler, Daniel W. Davies, Hugh Cartwright et al.

Nature 10.1038/s41586-018-0337-2

[2]

10.1021/acs.accounts.0c00785

[3]

Machine learning for a sustainable energy future

Zhenpeng Yao, Yanwei Lum, Andrew Johnston et al.

Nature Reviews Materials 10.1038/s41578-022-00490-5

[4]

Scaling deep learning for materials discovery

Amil Merchant, Simon Batzner, Samuel S. Schoenholz et al.

Nature 10.1038/s41586-023-06735-9

[5]

An autonomous laboratory for the accelerated synthesis of inorganic materials

Nathan J. Szymanski, Bernardus Rendy, Yuxing Fei et al.

Nature 10.1038/s41586-023-06734-w

[6]

10.1038/s41586-024-07892-1

[7]

10.1016/j.cpc.2022.108405

[8]

Discovery of Crystallizable Organic Semiconductors with Machine Learning

Holly M. Johnson, Filipp Gusev, Jordan T. Dull et al.

Journal of the American Chemical Society 10.1021/jacs.4c05245

[9]

10.1021/acscentsci.3c01009

[10]

10.1021/jacs.3c13491

[11]

10.1021/acsenergylett.4c02868

[12]

10.1126/science.abo4940

[13]

Discovery of Crystallizable Organic Semiconductors with Machine Learning

Holly M. Johnson, Filipp Gusev, Jordan T. Dull et al.

Journal of the American Chemical Society 10.1021/jacs.4c05245

[14]

10.1145/3340531.3416020

[15]

10.1016/j.coelec.2022.101136

[16]

10.1016/j.nanoen.2016.04.017

[17]

10.1515/zpch-1899-3004

[18]

10.1515/zpch-1905-5043

[19]

10.1098/rspa.1931.0187

[20]

10.1039/c9fd00032a

[21]

10.1039/d0cp01052a

[22]

10.1002/anie.201502438

[23]

10.1002/anie.201704021

[24]

10.1016/j.cattod.2023.114140

[25]

10.1038/s41467-022-34594-x

[26]

10.1039/c3an01647a

[27]

10.1149/1.2423772

[28]

Work function, electronegativity, and electrochemical behaviour of metals

Sergio Trasatti

Journal of Electroanalytical Chemistry and Interfa... 10.1016/s0022-0728(72)80485-6

[29]

10.1149/1.2427994

[30]

The stability number as a metric for electrocatalyst stability benchmarking

Simon Geiger, Olga Kasian, Marc Ledendecker et al.

Nature Catalysis 10.1038/s41929-018-0085-6

[31]

10.1002/celc.201402262

[32]

10.1595/205651323x16848455435118

[33]

10.1039/tf9595500999

[34]

10.1016/0022-0728(92)80162-w

[35]

“The Fe Effect”: A review unveiling the critical roles of Fe in enhancing OER activity of Ni and Co based catalysts

Sengeni Anantharaj, Subrata Kundu, Suguru Noda

Nano Energy 10.1016/j.nanoen.2020.105514

[36]

10.1021/jacs.8b05382

[37]

10.1021/acscatal.0c02252

[38]

10.1149/1.1838642

[39]

10.1016/j.elecom.2020.106867

[40]

10.1021/acsenergylett.4c01942

[41]

iR Compensation for Electrocatalysis Studies: Considerations and Recommendations

Weiran Zheng

ACS Energy Letters 10.1021/acsenergylett.3c00366

[42]

10.1021/acsami.1c21038

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Citations

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References

Details

Published: May 23, 2025
Vol/Issue: 1(9)
Pages: 1871-1877
License: View

Authors

A

Abraham Castro Garcia

Research Center for Energy and Environmental Materials, National Institute for Materials Science, 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan

K

Ken Sakaushi

Research Center for Energy and Environmental Materials, National Institute for Materials Science, 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan; Churchill College, Storey’s Way, Cambridge CB3 0DS, United Kingdom; Cavendish Laboratory, University of Cambridge, JJ Thomson Avenue, Cambridge CB3 0HE, United Kingdom

Funding

Japan Science and Technology Agency Award: JPMJAP2421

University of Cambridge

National Institute for Materials Science

Churchill College, University of Cambridge

Cite This Article

Abraham Castro Garcia, Ken Sakaushi (2025). Pitfalls in Artificial Intelligence Powered Discovery Due to Electrocatalyst Evaluation Methodologies. ACS Electrochemistry, 1(9), 1871-1877. https://doi.org/10.1021/acselectrochem.5c00153

Pitfalls in Artificial Intelligence Powered Discovery Due to Electrocatalyst Evaluation Methodologies

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