| Abstract Scope |
Nanomaterial characterization traditionally requires extensively trained experts and remains challenging even for specialists examining novel 2D structures. We present ATOMIC (Autonomous Technology for Optical Microscopy & Intelligent Characterization), a zero-shot framework integrating foundation models for fully autonomous characterization of 2D materials. Our system combines the Segment Anything Model, large language models, unsupervised clustering, and topological analysis to automate microscope control, sample scanning, image segmentation, and intelligent analysis through prompt engineering—without additional training. When analyzing MoS2 samples, our approach achieves 99.7% segmentation accuracy for single-layer identification, equivalent to human experts, while detecting grain boundary slits invisible to the human eye. The system maintains robust performance despite variable conditions including defocus, color temperature, and exposure variations. ATOMIC works across common 2D materials (graphene, MoS2, WSe2, SnSe) regardless of fabrication method, establishing a scalable, data-efficient paradigm that transforms nanoscale materials research. |