Bridging electron microscopy and materials analysis with an autonomous agentic platform

Electron microscopy (EM) reveals atomic-scale structures that underpin catalysis, energy storage, and semiconductor reliability, yet current workflows remain fragmented across segmentation, crystallographic reconstruction, property modeling, and literature review, often requiring weeks of expert effort. Although recent artificial intelligence models have assisted individual steps, the diversity of EM modalities and tasks means existing approaches remain siloed and perform poorly in complex multistage workflows. We present EMSeek, a modular, provenance-tracked multiagent platform that connects EM to materials insight through five key units: reference-guided one-for-all segmentation, mask-aware reconstruction of crystal structures from EM data, a gated mixture of experts property predictor with uncertainty calibration, literature retrieval with citation anchoring, and physical consistency checks with audit-ready reporting. These units are orchestrated by large language models (LLMs) that automatically plan, invoke, and execute tools, minimizing human intervention. On 20 material systems and five tasks, EMSeek delivers segmentation about twice as fast as Segment Anything with higher accuracy, achieves more than 90% structural similarity on STEM2Mat, and, with about 2% labeled calibration, matches or surpasses strong single experts on three out-of-distribution property benchmarks. A complete query runs in 2 to 5 minutes per image, roughly 50 times faster than expert workflows. Case studies on two-dimensional lattices and nanoparticles validate EMSeek's ability to automate complex workflows, with integrated uncertainty calibration and audit signals that provide scientists with rigorous yet actionable guidance to accelerate materials discovery.