Batteries, as complex materials systems, pose unique challenges for the application of machine learning. Although a shift to data-driven, machine learning-based battery research has started, new initiatives in academia and industry are needed to fully exploit its potential.
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Acknowledgements
We acknowledge the fruitful conversations with Dr. Brian Storey and Dr. Chirranjeevi Balaji Gopal.
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The authors contributed equally to all aspects of the article.
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MA, PK and AA have U.S. patent applications related to machine learning and batteries.
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Battery 2030+: https://battery2030.eu/
Battery500 Consortium: https://energystorage.pnnl.gov/battery500.asp
Joint Center for Energy Storage Research: https://www.jcesr.org/
ReCell Center: https://recellcenter.org/
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Aykol, M., Herring, P. & Anapolsky, A. Machine learning for continuous innovation in battery technologies. Nat Rev Mater 5, 725–727 (2020). https://doi.org/10.1038/s41578-020-0216-y
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DOI: https://doi.org/10.1038/s41578-020-0216-y
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