Purushottam Raj Purohit Ravi Raj Purohit, Tardif Samuel, Castelnau Olivier, Eymery Joel, Guinebretière René, Robach Odile, Ors Taylan, Micha Jean-Sébastien
Univ. Grenoble Alpes, CEA, IRIG, MEM, NRS, 17 rue des Martyrs, Grenoble 38000, France.
PIMM, Arts et Metiers Institute of Technology, CNRS, ENSAM, 151 boulevard de l'hopital, Paris 75013, France.
J Appl Crystallogr. 2022 Jun 15;55(Pt 4):737-750. doi: 10.1107/S1600576722004198. eCollection 2022 Aug 1.
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 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.
提出了一种基于前馈神经网络的模型，用于实时索引同步加速器X射线劳厄微衍射实验中记录的衍射斑点。应用数据降维从二维X射线衍射劳厄图像中提取物理一维特征，从而能够针对任何晶体系统即时训练神经网络。通过三个示例展示了劳厄神经网络（LaueNN）模型的能力：一个两相纳米结构、一个变形的织构高对称试样以及一种多晶低对称材料。这项工作提供了一种新颖的方法，能够在不到1秒的时间内高效索引简单和复杂记录图像中的劳厄斑点，从而为实现同步加速器劳厄衍射数据的实时分析开辟了道路。
