Title: AutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool Use
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: AI for Science, Agent-Based System, Scanning Transmission Electron Microscopy, Crystal Structure Reconstruction and property prediction

Abstract: Machine learning models for interatomic potentials and force fields require high-quality structural data, yet experimental crystal structures remain limited, creating a critical gap between computational simulations and real structures. While atomic-resolution electron microscopy can provide valuable images, converting these into simulation-ready structures is time-consuming and error-prone. We present **AutoMat**, an agent-based framework that directly converts Scanning Transmission Electron Microscopy (STEM) images to atomistic crystal structures for property prediction. The framework adaptively calls tools, including pattern-adaptive denoising, physics-guided template retrieval, symmetry-aware atomic reconstruction, and property prediction, enabling closed-loop reasoning from "image → structure → property." For systematic evaluation, we introduce **STEM2Mat-Bench**, a benchmark dataset containing 450+ annotated samples. Performance is assessed using lattice root-mean-square deviation (RMSD), formation energy mean absolute error (MAE), and structure matching accuracy.  Results demonstrate that AutoMat outperforms existing approaches including SOTA models, specialized domain tools, and closed-source multimodal large models. This work establishes a direct pathway from microscopic characterization to atomic-scale modeling, addressing a fundamental challenge in materials science.