A visual language model enabling intelligent nanomaterial scanning electron micrograph annotation

contrastive learning on SEM image-text pairs extracted from the literature. SEM-VLM demonstrates superior cross-modal retrieval performance over the general-domain model Contrastive Language-Image Pretraining (CLIP) and random baselines with Recall@10 and Recall@50 metrics, and keyword searches show its robust capability to retrieve relevant images. SEM-VLM also achieves high accuracy in zero-shot classification through ensemble vision-language alignment, outperforming CLIP. In few-shot settings, SEM-VLM with 2.1% training labels exhibits superior performance compared with the fully supervised model (EMCNet: Graph-Nets for Electron Micrograph Classification). Activation mapping analysis reveals precise localization of critical nanoscale features (particles, holes, and probe tips), providing more interpretable results than conventional approaches while maintaining operational reliability. This multimodal framework reduces labeled dataset dependency by orders of magnitude and enables automated high-precision classification.