期刊
JOURNAL OF APPLIED CRYSTALLOGRAPHY
卷 55, 期 -, 页码 737-750出版社
INT UNION CRYSTALLOGRAPHY
DOI: 10.1107/S1600576722004198
关键词
synchrotron X-ray Laue microdiffraction; neural networks; hkl recognition
资金
- Agence Nationale de la Recherche
- DFG [ANR-19CE09-0035-01]
A feed-forward neural network model is presented to index diffraction spots recorded during synchrotron X-ray Laue microdiffraction experiments in real time. The model applies data dimensionality reduction to extract physical features from 2D X-ray diffraction Laue images, enabling on-the-fly training for any crystal system. The capabilities of the model are demonstrated with three examples, showcasing its ability to efficiently index Laue spots in simple and complex recorded images.
A feed-forward neural-network-based model is presented to index, in real time, the diffraction spots recorded during synchrotron X-ray Laue microdiffraction experiments. Data dimensionality reduction is applied to extract physical 1D features from the 2D X-ray diffraction Laue images, thereby making it possible to train a neural network on the fly for any crystal system. The capabilities of the LaueNN model are illustrated through three examples: a two-phase nanostructure, a textured high-symmetry specimen deformed in situ and a polycrystalline low-symmetry material. This work provides a novel way to efficiently index Laue spots in simple and complex recorded images in <1 s, thereby opening up avenues for the realization of real-time analysis of synchrotron Laue diffraction data.
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