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

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 nano-structure, 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.

SUBMITTER: Purushottam Raj Purohit RRP

PROVIDER: S-EPMC9348891 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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LaueNN: neural-network-based <i>hkl</i> recognition of Laue spots and its application to polycrystalline materials.

Purushottam Raj Purohit Ravi Raj Purohit RRP Tardif Samuel S Castelnau Olivier O Eymery Joel J Guinebretière René R Robach Odile O Ors Taylan T Micha Jean-Sébastien JS

Journal of applied crystallography 20220615 Pt 4

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 nano-structure, a textured high-symme ...[more]

PMID: 35974740

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