SOURCE-LINKED INTELLIGENCE

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

arXiv · AI, language, vision and robotics · article · Aug 27, 2026 · UTC

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

Read original source ↗ Open in workspace

recordType

paper

region

Global