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Schertzer et al. J. Mater. Inf. 2025, 5, 5 https://dx.doi.org/10.20517/jmi.2024.69 Page 15 of 17

Provided insights into the theoretical and experimental details of the AEM systems: Lively, R. P., Sampath,
J., Asl-Otmi, M.
Designed, trained and evaluated the machine learning models and provided insight into the theoretical and
experimental details of the AEM systems: Sose, A.
All authors discussed the results and commented on the manuscript.

Availability of data and materials
The dataset used in this work and the property predictions for top-performing candidates are available on
the polyVERSE GitHub.

Financial support and sponsorship
This work was supported as part of the UNCAGE-ME, an Energy Frontier Research Center funded by the
U.S. Department of Energy, Office of Science, Basic Energy Sciences at the Georgia Institute of Technology
under award # DE-SC0012577.

Conflicts of interest
All authors declared that there are no conflicts of interest.

Ethical approval and consent to participate
Not applicable.

Consent for publication
Not applicable.

Copyright
© The Author(s) 2025.

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