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Machine learning for continuous innovation in battery technologies

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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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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Correspondence to Muratahan Aykol.

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Competing interests

MA, PK and AA have U.S. patent applications related to machine learning and batteries.

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Related links

Battery 2030+:

Battery500 Consortium:

Joint Center for Energy Storage Research:

ReCell Center:

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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).

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