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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Ramadesigan, V. et al. Modeling and simulation of lithium-ion batteries from a systems engineering perspective. J. Electrochem. Soc. 159, R31–R45 (2012).
Ward, L. et al. Strategies for accelerating the adoption of materials informatics. MRS Bull. 43, 683–689 (2018).
Ahmad, Z., Xie, T., Maheshwari, C., Grossman, J. C. & Viswanathan, V. Machine learning enabled computational screening of inorganic solid electrolytes for suppression of dendrite formation in lithium metal anodes. ACS Cent. Sci. 4, 996–1006 (2018).
Sendek, A. D. et al. machine learning-assisted discovery of solid Li-ion conducting materials. Chem. Mater. 31, 342–352 (2019).
Severson, K. A. et al. Data-driven prediction of battery cycle life before capacity degradation. Nature Energy 4, 383–391 (2019).
Attia, P. et al. Closed-loop optimization of extreme fast charging for batteries using machine learning. Nature 578, 397–402 (2020).
You, G.-W., Park, S. & Oh, D. Real-time state-of-health estimation for electric vehicle batteries: A data-driven approach. Appl. Energy 176, 92–103 (2016).
Herring, P. K. et al. BEEP: a python library for battery evaluation and early prediction. SoftwareX 11, 100506 (2020).
Aykol, M. et al. The materials research platform: defining the requirements from user stories. Matter 1, 1433–1438 (2019).
Marks, T., Trussler, S., Smith, A. J., Xiong, D. & Dahn, J. R. A guide to Li-ion coin-cell electrode making for academic researchers. J. Electrochem. Soc. 158, A51–A57 (2011).
We acknowledge the fruitful conversations with Dr. Brian Storey and Dr. Chirranjeevi Balaji Gopal.
MA, PK and AA have U.S. patent applications related to machine learning and batteries.
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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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