A novel data-driven approach is proposed for analyzing synchrotron Laue X-ray microdiffraction scans based on machine learning algorithms. The basic architecture and major components of the method are formulated mathematically. It is demonstrated through typical examples including polycrystalline BaTiO3, multiphase transforming alloys and finely twinned martensite. The computational pipeline is implemented for beamline 12.3.2 at the Advanced Light Source, Lawrence Berkeley National Laboratory. The conventional analytical pathway for X-ray diffraction scans is based on a slow pattern-by-pattern crystal indexing process. This work provides a new way for analyzing X-ray diffraction 2D patterns, independent of the indexing process, and motivates further studies of X-ray diffraction patterns from the machine learning perspective for the development of suitable feature extraction, clustering and labeling algorithms.

中文翻译：

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同步加速器 X 射线劳厄微衍射扫描分析的数据驱动方法

提出了一种新的数据驱动方法，用于基于机器学习算法分析同步加速器劳厄 X 射线微衍射扫描。该方法的基本架构和主要组成部分均以数学方式表述。通过多晶BaTiO等典型实例进行论证3、多相转变合金和细孪晶马氏体。计算管道是在劳伦斯伯克利国家实验室的先进光源中针对光束线 12.3.2 实现的。X 射线衍射扫描的传统分析途径基于缓慢的逐图案晶体索引过程。这项工作为分析 X 射线衍射二维图案提供了一种独立于索引过程的新方法，并激发了从机器学习角度对 X 射线衍射图案的进一步研究，以开发合适的特征提取、聚类和标记算法。