Discovery Learning predicts battery cycle life from minimal experiments
Figgener, J. et al. Multi-year field measurements of home storage systems and their use in capacity estimation. Nat. Energy 9, 1438–1447 (2024).
Google Scholar
Degen, F., Winter, M., Bendig, D. & Tübke, J. Energy consumption of current and future production of lithium-ion and post lithium-ion battery cells. Nat. Energy 8, 1284–1295 (2023).
Google Scholar
Severson, K. A. et al. Data-driven prediction of battery cycle life before capacity degradation. Nat. Energy 4, 383–391 (2019).
Google Scholar
Attia, P. M. et al. Closed-loop optimization of fast-charging protocols for batteries with machine learning. Nature 578, 397–402 (2020).
Google Scholar
Settles, B. Active Learning Literature Survey. University of Wisconsin–Madison (2009).
Yu, R. & Wang, R. Learning dynamical systems from data: an introduction to physics-guided deep learning. Proc. Natl Acad. Sci. 121, e2311808121 (2024).
Google Scholar
Xian, Y., Lampert, C. H., Schiele, B. & Akata, Z. Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly. IEEE Trans Pattern Anal. Mach. Intell. 41, 2251–2265 (2019).
Google Scholar
Wang, H. et al. Scientific discovery in the age of artificial intelligence. Nature 620, 47–60 (2023).
Google Scholar
Chen, S. et al. External Li supply reshapes Li deficiency and lifetime limit of batteries. Nature 638, 676–683 (2025).
Google Scholar
Lam, V. N. et al. A decade of insights: delving into calendar aging trends and implications. Joule 9, 101796 (2025).
Google Scholar
Zhu, Y., Gu, X., Liu, K., Zhao, W. & Shang, Y. Rapid test and assessment of lithium-ion battery cycle life based on transfer learning. IEEE Trans. Transp. Electrification 10, 9133–9143 (2024).
Google Scholar
Edge, J. S. et al. Lithium ion battery degradation: what you need to know. Phys. Chem. Chem. Phys. 23, 8200–8221 (2021).
Google Scholar
Zhang, H. et al. Battery lifetime prediction across diverse ageing conditions with inter-cell deep learning. Nat. Mach. Intell. 7, 270–277 (2025).
Google Scholar
Guo, N. et al. Semi-supervised learning for explainable few-shot battery lifetime prediction. Joule 8, 1820–1836 (2024).
Google Scholar
Aykol, M., Herring, P. & Anapolsky, A. Machine learning for continuous innovation in battery technologies. Nat. Rev. Mater. 5, 725–727 (2020).
Google Scholar
Ward, L. et al. Principles of the Battery Data Genome. Joule 6, 2253–2271 (2022).
Google Scholar
Merchant, A. et al. Scaling deep learning for materials discovery. Nature 624, 80–85 (2023).
Google Scholar
Bruner, J. S. The act of discovery. Harvard Educ. Rev. 31, 21–32 (1961).
Preger, Y. et al. Degradation of commercial lithium-ion cells as a function of chemistry and cycling conditions. J. Electrochem. Soc. 167, 120532 (2020).
Google Scholar
Lain, M. J., Brandon, J. & Kendrick, E. Design strategies for high power vs. high energy lithium ion cells. Batteries 5, 64 (2019).
Google Scholar
Trad, K. Lifecycle ageing tests on commercial 18650 Li ion cell @ 25 °C and 45 °C. 4TU. ResearchData (2021).
Heenan, T. M. M. et al. An advanced microstructural and electrochemical datasheet on 18650 Li-ion batteries with nickel-rich NMC811 cathodes and graphite-silicon anodes. J. Electrochem. Soc. 167, 140530 (2020).
Google Scholar
Zhu, J. et al. Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation. Nat. Commun. 13, 2261 (2022).
Google Scholar
Wildfeuer, L. et al. Experimental degradation study of a commercial lithium-ion battery. J. Power Sources 560, 232498 (2023).
Google Scholar
Bills, A. et al. A battery dataset for electric vertical takeoff and landing aircraft. Sci. Data 10, 344 (2023).
Google Scholar
Yang, X.-G., Liu, T. & Wang, C.-Y. Thermally modulated lithium iron phosphate batteries for mass-market electric vehicles. Nat. Energy 6, 176–185 (2021).
Google Scholar
Kim, J.-H. et al. Kosmotropic aqueous processing solution for green lithium battery cathode manufacturing. Nat. Commun 16, 1686 (2025).
Google Scholar
Ko, S. et al. Rapid safety screening realized by accelerating rate calorimetry with lab-scale small batteries. Nat. Energy 10, 707–714 (2025).
Google Scholar
Wang, C.-Y. et al. Fast charging of energy-dense lithium-ion batteries. Nature 611, 485–490 (2022).
Google Scholar
Zhang, J., Che, Y., Teodorescu, R., Song, Z. & Hu, X. Energy storage management in electric vehicles. Nat. Rev. Clean Technol. 1, 161–175 (2025).
Google Scholar
Szymanski, N. J. et al. An autonomous laboratory for the accelerated synthesis of novel materials. Nature 624, 86–91 (2023).
Google Scholar
Angello, N. H. et al. Closed-loop transfer enables artificial intelligence to yield chemical knowledge. Nature 633, 351–358 (2024).
Google Scholar
Cranmer, K., Brehmer, J. & Louppe, G. The frontier of simulation-based inference. Proc. Natl Acad. Sci. 117, 30055–30062 (2020).
Google Scholar
Brehmer, J. Simulation-based inference in particle physics. Nat. Rev. Phys. 3, 305–305 (2021).
Google Scholar
Durkan, C., Bekasov, A., Murray, I. & Papamakarios, G. Neural spline flows. In Proc. Advances in Neural Information Processing Systems 32 (Curran Associates, 2019).
Tejero-Cantero, A. et al. sbi: a toolkit for simulation-based inference. J. Open Source Softw. 5, 2505 (2020).
Google Scholar
Sulzer, V., Marquis, S. G., Timms, R., Robinson, M. & Chapman, S. J. Python battery mathematical modelling (PyBaMM). J. Open Res. Softw. 9, 14 (2021).
Google Scholar
Lake, B. M. & Baroni, M. Human-like systematic generalization through a meta-learning neural network. Nature 623, 115–121 (2023).
Google Scholar
Greenberg, D., Nonnenmacher, M. & Macke, J. Automatic posterior transformation for likelihood-free inference. In Proc. 36th International Conference on Machine Learning 2404–2414 (PMLR, 2019).
Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B Stat. Methodol. 67, 301–320 (2005).
Google Scholar
Awad, M. & Khanna, R. in Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers (eds Awad, M. & Khanna, R.) 67–80 (Apress, 2015).
Pedregosa, F. et al. scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Google Scholar
Schulz, E., Speekenbrink, M. & Krause, A. A tutorial on Gaussian process regression: modelling, exploring, and exploiting functions. J. Math. Psychol. 85, 1–16 (2018).
Google Scholar
Zhang, J. et al. Discovery Learning predicts battery cycle life from minimal experiments. Zenodo (2025).
link
