ArticleJournal of applied crystallography2022
LaueNN: neural-network-based
Article in Journal of applied crystallography, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
11 citing papers in PubMed.
- Deterministic segmentation of grains in dense Laue microdiffraction datasets.Journal of applied crystallography · 2026Article
- Deformation-Induced Formation of Stray Grains in Additive Manufacturing of Single Crystals.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Article
- Physics-informed machine learning analysis for nanoscale grain mapping by synchrotron Laue microdiffraction.Journal of applied crystallography · 2025Article
- Laue-DIALS: Open-source software for polychromatic x-ray diffraction data.Structural dynamics (Melville, N.Y.) · 2024Article
- Laue-DIALS: open-source software for polychromatic X-ray diffraction data.bioRxiv : the preprint server for biology · 2024Article
- Neural networks for rapid phase quantification of cultural heritage X-ray powder diffraction data.Journal of applied crystallography · 2024Article
- Application of laboratory micro X-ray fluorescence devices for X-ray topography.Journal of applied crystallography · 2024Article
- Laue microdiffraction on polycrystalline samples above 1500 K achieved with the QMAX-µLaue furnace.Journal of applied crystallography · 2024Article
- UpgradedJournal of applied crystallography · 2023Article
- CNN-Based Laue Spot Morphology Predictor for Reliable Crystallographic Descriptor Estimation.Materials (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.