Context-Aware Deep Learning for Defect Classification in Atomic-Resolution STEM 事件

Context-Aware Deep Learning for Defect Classification in Atomic-Resolution STEM arXiv:2606.09419v1 Announce Type: cross Abstract: Artificial intelligence is rapidly advancing materials characterization, yet most applications in electron microscopy rely solely on image contrast, overlooking the chemical and experimental context that shapes image formation. This limitation makes defect classification inherently ambiguous, as similar contrasts can arise from different materials or imaging conditio