Data-efficient crack quantification in lithium-ion cathodes using foundation model transfer

Abstract:Battery lifetime is central to sustainable electrification, yet the particle cracking that drives lithium-ion cathode aging is hard to measure: quantitative microscopy of this degradation is bottlenecked by annotation, because each destructive electron-microscopy cross-section spans hundreds of megapixels and pixel-level expert labelling requires hours per image. We show that a frozen self-supervised vision-transformer encoder, combined with a lightweight trainable decoder and iterative model-assisted annotation, turns this sparse labelling budget into population-scale degradation measurements. Applied to three 120-megapixel NMC cathode cross-sections representing initial, cycled-aged and calendar-aged states, the framework distinguishes intragranular cracks from early- and late-stage intergranular cracks and yields per-particle distributions of crack width, tortuosity and area fraction. Late intergranular crack coverage reaches 4.6% in the cycled sample versus 0.5% in the initial and calendar-aged samples, forming more tortuous, higher-coverage networks, consistent with degradation from repeated electrochemical cycling rather than elevated-temperature storage alone. A single destructive image yields the population-level statistics needed for lifetime-extending design, aging assessment and second-life decisions.

38 pages, 15 figures, 2 tables. Supplementary Information included as Appendix B. Under review at Energy Storage Materials

Materials Science (cond-mat.mtrl-sci); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)

arXiv:2608.27162 [cond-mat.mtrl-sci]

(or arXiv:2608.27162v1 [cond-mat.mtrl-sci] for this version)

https://doi.org/10.48550/arXiv.2608.27162 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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