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Diffraction-Native Representation Learning for Automatic Symmetry Recognition and Structural Retrieval
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DOI：10.1002/adfm.75949.png)
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Abstract
En 中文
Selected area electron diffraction (SAED) is an indispensable tool for local crystallographic analysis, yet its interpretation remains a time-consuming bottleneck, particularly when multiple compounds exhibit comparable crystal structures. Conventional coding-based approaches struggle with complex diffraction geometries, missing reflections, and orientation ambiguities, while existing deep learning (DL) methods are largely constrained to single-view inputs and fixed classification categories rather than explicit structure identification. A multi-view convolutional neural network with task-specific branches is presented, in which diffraction information from two zone axes is fused into a unified diffraction-native descriptor to enable both symmetry inference and database-scale structural identification. Trained on ~10 000 materials and evaluated on ~2000 unseen materials from the Materials Project database, the proposed method achieves 95.09% accuracy in crystal system classification and 80.12% accuracy in space group prediction. Under chemical formula constraints, it further attains a Top-1 recall of 93.9% for structure retrieval across ∼13 000 candidate materials. The model further demonstrates strong robustness across simulated zeolite structures, in situ phase-transition datasets capturing dynamic symmetry evolution, and experimentally obtained polymorph datasets. This descriptor-driven paradigm enables rapid in situ analysis (30–50 s) with interpretable and database-compatible outputs, offering a scalable framework for automated crystallographic analysis and materials discovery.
Keywords:
crystal structure identification
descriptor-based structure identification
multi-view branch convolutional neural network
selected area electron diffraction
transmission electron microscopy
Journal
IF:
19
Papers:
3.4W
Citations:
32.1W
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