This video explores a groundbreaking approach using closed-loop machine learning to discover fast-charging protocols that double battery cycle life — in a fraction of the time. Based on a 2020 Nature study, we dive into how AI accelerates discovery, challenges assumptions, and reshapes the future of battery technology.
Key research paper referenced:
-Attia, P.M., Grover, A., Jin, N. et al. Closed-loop optimization of fast-charging protocols for batteries with machine learning. Nature 578, 397–402 (2020).
DOI: https://doi.org/10.1038/s41586-020-1994-5
-Severson, K.A., Attia, P.M., Jin, N. et al. Data-driven prediction of battery cycle life before capacity degradation. Nat Energy 4, 383–391 (2019).
DOI: https://doi.org/10.1038/s41560-019-0356-8
-Wang, H., Fu, T., Du, Y. et al. Scientific discovery in the age of artificial intelligence. Nature 620, 47–60 (2023).
DOI: https://doi.org/10.1038/s41586-023-06221-2
-B. P. MacLeod et al. ,Self-driving laboratory for accelerated discovery of thin-film materials.Sci. Adv.6,eaaz8867(2020)
DOI: https://doi.org/10.1126/sciadv.aaz8867
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