Machine Learning Automated Analysis of Enormous Synchrotron X-ray Diffraction DatasetsClick to copy article linkArticle link copied!
- Xiaodong ZhaoXiaodong ZhaoPacific Northwest National Laboratory, Richland, Washington 99354, United StatesDepartment of Chemistry, Washington State University, Pullman 99164, United StatesMore by Xiaodong Zhao
- YiXuan LuoYiXuan LuoDepartment of Electrical and Computer Engineering, University of Rochester, New York 14627, United StatesMore by YiXuan Luo
- Juejing LiuJuejing LiuPacific Northwest National Laboratory, Richland, Washington 99354, United StatesMaterials Science and Engineering Program, Washington State University, Pullman 99164, United StatesMore by Juejing Liu
- Wenjun LiuWenjun LiuAdvanced Photon Source, Argonne National Laboratory, Lemont 60439, United StatesMore by Wenjun Liu
- Kevin M. RossoKevin M. RossoPacific Northwest National Laboratory, Richland, Washington 99354, United StatesMore by Kevin M. Rosso
- Xiaofeng Guo*Xiaofeng Guo*[email protected]Department of Chemistry, Washington State University, Pullman 99164, United StatesMaterials Science and Engineering Program, Washington State University, Pullman 99164, United StatesMore by Xiaofeng Guo
- Tong Geng*Tong Geng*[email protected]Department of Electrical and Computer Engineering, University of Rochester, New York 14627, United StatesMore by Tong Geng
- Ang Li*Ang Li*[email protected]Pacific Northwest National Laboratory, Richland, Washington 99354, United StatesMore by Ang Li
- Xin Zhang*Xin Zhang*[email protected]Pacific Northwest National Laboratory, Richland, Washington 99354, United StatesMore by Xin Zhang
Abstract
X-ray diffraction (XRD) data analysis can be a time-consuming and laborious task. Deep neural network (DNN) based models trained with synthetic XRD patterns have been proven to be a highly efficient, accurate, and automated method for analyzing common XRD data collected from solid samples in ambient environments. However, it remains unclear whether synthetic XRD-based models can be effective in solving micro(μ)-XRD mapping data for in situ experiments involving liquid phases, which always have lower quality and significant artifacts. In this study, we collected μ-XRD mapping data from a LaCl3-calcite hydrothermal fluid system and trained two categories of models to analyze the experimental XRD patterns. The models trained solely with synthetic XRD patterns showed low accuracy (as low as 64%) when solving experimental μ-XRD mapping data. However, the accuracy of the DNN models significantly improved (90% or above) when we trained them with a data set containing both synthetic and a small number of labeled experimental μ-XRD patterns. This study highlights the importance of labeled experimental patterns in training DNN models to solve μ-XRD mapping data from in situ experiments involving liquid phases.
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