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Machine learning for continuous innovation in battery technologies

Nature Reviews Materials volume 5, pages 725–727 (2020)Cite this article

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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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Authors and Affiliations

  1. Toyota Research Institute, Los Altos, CA, USA

    Muratahan Aykol, Patrick Herring & Abraham Anapolsky

Authors
  1. Muratahan Aykol
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  2. Patrick Herring
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  3. Abraham Anapolsky
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Contributions

The authors contributed equally to all aspects of the article.

Corresponding author

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+: 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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