Data-driven approach for synchrotron X-ray Laue microdiffraction scan analysis
| Autor: | Nobumichi Tamura, Mostafa Karami, Xian Chen, Yintao Song, Chenbo Zhang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Diffraction
Computer science Pipeline (computing) Feature extraction FOS: Physical sciences 02 engineering and technology unsupervised learning 01 natural sciences Biochemistry law.invention Computational science Inorganic Chemistry Structural Biology law 0103 physical sciences General Materials Science property maps Physical and Theoretical Chemistry Cluster analysis synchrotron X-ray microdiffraction 010302 applied physics data-driven analysis Condensed Matter - Materials Science Search engine indexing Materials Science (cond-mat.mtrl-sci) 021001 nanoscience & nanotechnology Condensed Matter Physics Synchrotron PCA labeler Beamline Physics - Data Analysis Statistics and Probability Unsupervised learning 0210 nano-technology Data Analysis Statistics and Probability (physics.data-an) |
| Zdroj: | Acta crystallographica. Section A, Foundations and advances, vol 75, iss Pt 6 |
| Popis: | 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. 29 pages, 25 figures under the second round of review by Acta Crystallographica A |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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