  We propose a novel data-driven approach 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.
We demonstrate it through typical examples including polycrystalline BaTiO$_3$,
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 Lab. 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 prospective for the
development of suitable feature extraction, clustering and labeling algorithms.
