LaueNN: neural‐network‐based hkl recognition of Laue spots and its application to polycrystalline materials. Issue 4 (15th June 2022)

• Record Type: Journal Article
• Authors: Purushottam Raj Purohit, Ravi Raj Purohit; Tardif, Samuel; Castelnau, Olivier; Eymery, Joel; Guinebretière, René; Robach, Odile; Ors, Taylan; Micha, Jean-Sébastien
• Is Part Of: Journal of applied crystallography. Volume 55:Issue 4(2022)
• Page Start: 737
• Page End: 750
• Publication Date: 2022-06-15
• Subjects: synchrotron X‐ray Laue microdiffraction -- neural networks -- hkl recognition; Crystallography -- Periodicals; 548.05
• DOI: 10.1107/S1600576722004198
• Languages: English
• ISSNs: 0021-8898

Abstract

Abstract : An efficient neural network architecture to index Laue spots in synchrotron Laue microdiffraction patterns is presented. The efficiency and predictive capability of the model makes it applicable to real‐time indexing of Laue microdiffraction data. 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.