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LaueNN: neural-network-based hkl recognition of Laue spots and its application to polycrystalline materials

Article dans une revue avec comité de lecture
Author
PURUSHOTTAM RAJ PUROHIT, Ravi Raj Purohit
1059495 Université de Grenoble
TARDIF, Samuel
555292 Institut de Recherche Interdisciplinaire de Grenoble [IRIG]
1059495 Université de Grenoble
CASTELNAU, Olivier
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
EYMERY, Joel
300016 Commissariat à l'énergie atomique et aux énergies alternatives [CEA]
GUINEBRETIÈRE, René
522021 Institut de Recherche sur les CERamiques [IRCER]
ROBACH, Odile
1043131 Nanostructures et Rayonnement Synchrotron [NRS ]
300016 Commissariat à l'énergie atomique et aux énergies alternatives [CEA]
ORS, Taylan
505910 Institut de Science des Matériaux de Mulhouse [IS2M]
MICHA, Jean-Sébastien
1043175 SYstèmes Moléculaires et nanoMatériaux pour l’Energie et la Santé [SYMMES]

URI
http://hdl.handle.net/10985/22223
DOI
10.1107/s1600576722004198
Date
2022-08
Journal
Journal of Applied Crystallography

Abstract

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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