Data-efficient crack quantification in lithium-ion cathodes using foundation model transfer
Read the original on arXiv Machine Learning →The paper presents a data‑efficient method for quantifying cracks in lithium‑ion cathodes using a frozen self‑supervised vision‑transformer encoder, a lightweight decoder, and iterative model‑assisted annotation. Applied to three 120‑megapixel NMC cathode cross‑sections, the framework distinguishes intragranular from early and late intergranular cracks, providing per‑particle distributions of crack width, tortuosity, and area fraction. Late intergranular crack coverage reaches 4.6% in cycled samples versus 0.5% in initial and calendar‑aged samples, indicating degradation primarily from electrochemical cycling.
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