Machine learning for interpreting coherent X-ray speckle patterns

被引:0
|
作者
Shen, Mingren [1 ]
Sheyfer, Dina [2 ]
Loeffler, Troy David [3 ]
Stephenson, G. Brian [4 ]
Sankaranarayanan, Subramanian K. R. S. [3 ,5 ]
Chan, Maria K. Y. [3 ]
Morgan, Dane [1 ]
机构
[1] Univ Wisconsin Madison, Dept Mat Sci & Engn, Madison, WI 53706 USA
[2] Argonne Natl Lab, X Ray Sci Div, Lemont, IL 60439 USA
[3] Argonne Natl Lab, Ctr Nanoscale Mat, Lemont, IL 60439 USA
[4] Argonne Natl Lab, Mat Sci Div, Lemont, IL 60439 USA
[5] Univ Illinois, Dept Mech & Ind Engn, Chicago, IL 60607 USA
关键词
Deep neural networks - Learning systems;
D O I
10.1016/j.commatsci.2023.112500
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Speckle patterns produced by coherent X-ray have a close relationship with the internal structure of materials but quantitative inversion of the relationship to determine structure from speckle patterns is challenging. Here, we investigate the link between coherent X-ray speckle patterns and sample structures using a model 2D disk system and explore the ability of machine learning to learn aspects of the relationship. Specifically, we train a deep neural network to classify the coherent Xray speckle patterns according to the disk number density in the corresponding structure. It is demonstrated that the classification system is accurate for both non disperse and disperse size distributions.
引用
收藏
页数:9
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