2019/09/14 by Yintao Song, Nobumichi Tamura, Chenbo Zhang +2 · 1 citation
Materials Science · Physics and Astronomy · #Advanced X-ray Imaging Techniques #Beamline #Diffraction #Machine Learning in Materials Science #Pipeline (software) #Synchrotron #Synchrotron radiation #X-ray Diffraction in Crystallography #X-ray crystallography #cond-mat.mtrl-sci #physics.data-an
paper · pdf · doi:10.1107/s2053273319012804
published as Acta Crystallographica Section A 2019 · 29 pages, 25 figures under the second round of review by Acta Crystallographica A
arxiv created 2019/09/14 · openalex created_date 2019/09/19 · openalex publication_date 2019/10/29 · arxiv updated 2020/10/12 · openalex updated_date 2026/08/05
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 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 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.