斯坦福团队用基础模型解决电池老化检测难题，一张图像就能获取全颗粒统计，比人工标注快数百倍。
研究人员使用冻结的自监督视觉Transformer编码器，结合轻量级可训练解码器和迭代模型辅助标注，将稀疏标注预算转化为大规模退化测量。该方法应用于三张120兆像素NMC阴极横截面图像，能够区分颗粒内裂纹和早期/晚期颗粒间裂纹。循环老化样品的晚期颗粒间裂纹覆盖率达到4.6%，而初始和日历老化样品仅为0.5%。
Data-efficient crack quantification in lithium-ion cathodes using foundation model transfer
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.