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AI Cracks Crystal Structure Reconstruction from Microscope Images with AutoMat

Beijing, China - In a significant leap for materials science, researchers at Tsinghua University and the Shanghai Artificial Intelligence Laboratory have developed AutoMat, an artificial intelligence (AI) system capable of automatically reconstructing atomic crystal structures from scanning transmission electron microscopy (STEM) images. This breakthrough, detailed in a recent paper, promises to accelerate the discovery and development of new materials by overcoming a major bottleneck in the field.

Machine learning (ML) models that predict material properties with near-quantum accuracy increasingly rely on accurate atomic structures. However, obtaining such structures experimentally is often a time-consuming and error-prone process, especially when using atomic-resolution electron microscopy data. Manually translating STEM images into simulation-ready formats has been a bottleneck, hindering the training and validation of these crucial ML models.

AutoMat addresses this challenge with an end-to-end, agent-assisted pipeline. It takes raw STEM images as input and, through a series of coordinated steps, outputs atomic crystal structures in standard crystallographic information file (CIF) format, along with predictions of their physical properties.

The system utilizes a Large Language Model (LLM) agent to orchestrate four key modules:

  1. Pattern-Adaptive STEM Image Denoising: A mixture-of-experts network called MOE-DIVAESR removes noise and enhances image quality, crucial for accurate downstream analysis. Think of it as a specialized filter that can recognize and enhance periodic patterns within the noisy microscope image.

  2. Physics-Guided Template Retrieval: The enhanced image is compared to a vast library of simulated STEM projections to find the best-matching candidate structure. Imagine having a database of molecular “blueprints” that AutoMat automatically searches through to find the best starting point for structure reconstruction.

  3. Symmetry-Constrained Structure Reconstruction: AutoMat identifies atomic positions and infers lattice parameters, guided by symmetry considerations. This step converts the ‘dots’ in the STEM image into a well-defined atomic arrangement, much like connecting the dots to reveal a picture.

  4. Energy Evaluation via Machine-Learned Potential: The reconstructed structure is then “relaxed” using a pre-trained machine learning model called MatterSim, which predicts the formation energy of the material. This helps confirm that the proposed structure is energetically stable and therefore physically realistic.

To rigorously test AutoMat’s performance, the researchers created STEM2Mat-Bench, a new benchmark dataset containing thousands of simulated STEM images and their corresponding crystal structures. The dataset is split into three tiers of increasing complexity, representing unary, binary, and ternary materials, each representing different degrees of difficulty in structure reconstruction.

Compared to existing tools and multimodal large language models like GPT-4 and Qwen-VL, AutoMat demonstrated significantly improved accuracy in reconstructing crystal structures and predicting their formation energies. For example, AutoMat reduced the error in energy predictions by an order of magnitude compared to these general-purpose models.

The success of AutoMat marks a significant step towards automating materials discovery. By bridging the gap between microscopy and atomistic simulation, it promises to accelerate the identification and development of novel materials for a wide range of applications. The code and dataset are available to the public, ensuring transparency and fostering further innovation in the field.