Data-driven approach for synchrotron X-ray Laue microdiffraction scan analysis
Abstract
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. Here, 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.
- Authors:
-
[3]
- Independent researcher, Foster City, CA (United States)
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Advanced Light Source (ALS)
- Hong Kong Univ. of Science and Technology (Hong Kong)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1580410
- Grant/Contract Number:
- AC02-05CH11231; 16207017; 26200316
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Acta Crystallographica. Section A, Foundations and Advances (Online)
- Additional Journal Information:
- Journal Name: Acta Crystallographica. Section A, Foundations and Advances (Online); Journal Volume: 75; Journal Issue: 6; Journal ID: ISSN 2053-2733
- Publisher:
- International Union of Crystallography
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 42 ENGINEERING; synchrotron X-ray microdiffraction; data-driven analysis; PCA labeler; unsupervised learning; property maps
Citation Formats
Song, Yintao, Tamura, Nobumichi, Zhang, Chenbo, Karami, Mostafa, and Chen, Xian. Data-driven approach for synchrotron X-ray Laue microdiffraction scan analysis. United States: N. p., 2019.
Web. doi:10.1107/s2053273319012804.
Song, Yintao, Tamura, Nobumichi, Zhang, Chenbo, Karami, Mostafa, & Chen, Xian. Data-driven approach for synchrotron X-ray Laue microdiffraction scan analysis. United States. https://doi.org/10.1107/s2053273319012804
Song, Yintao, Tamura, Nobumichi, Zhang, Chenbo, Karami, Mostafa, and Chen, Xian. Tue .
"Data-driven approach for synchrotron X-ray Laue microdiffraction scan analysis". United States. https://doi.org/10.1107/s2053273319012804. https://www.osti.gov/servlets/purl/1580410.
@article{osti_1580410,
title = {Data-driven approach for synchrotron X-ray Laue microdiffraction scan analysis},
author = {Song, Yintao and Tamura, Nobumichi and Zhang, Chenbo and Karami, Mostafa and Chen, Xian},
abstractNote = {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. Here, 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.},
doi = {10.1107/s2053273319012804},
journal = {Acta Crystallographica. Section A, Foundations and Advances (Online)},
number = 6,
volume = 75,
place = {United States},
year = {Tue Oct 29 00:00:00 EDT 2019},
month = {Tue Oct 29 00:00:00 EDT 2019}
}
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