A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic XRD powder patterns

Jin-Woong Lee, Woon Bae Park, Jin Hee Lee, Satendra Pal Singh and Kee-Sun Sohn ()
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Jin-Woong Lee: Sejong University
Woon Bae Park: Sejong University
Jin Hee Lee: Sejong University
Satendra Pal Singh: Sejong University
Kee-Sun Sohn: Sejong University

Nature Communications, 2020, vol. 11, issue 1, 1-11

Abstract: Abstract Here we report a facile, prompt protocol based on deep-learning techniques to sort out intricate phase identification and quantification problems in complex multiphase inorganic compounds. We simulate plausible powder X-ray diffraction (XRD) patterns for 170 inorganic compounds in the Sr-Li-Al-O quaternary compositional pool, wherein promising LED phosphors have been recently discovered. Finally, 1,785,405 synthetic XRD patterns are prepared by combinatorically mixing the simulated powder XRD patterns of 170 inorganic compounds. Convolutional neural network (CNN) models are built and eventually trained using this large prepared dataset. The fully trained CNN model promptly and accurately identifies the constituent phases in complex multiphase inorganic compounds. Although the CNN is trained using the simulated XRD data, a test with real experimental XRD data returns an accuracy of nearly 100% for phase identification and 86% for three-step-phase-fraction quantification.

Date: 2020
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DOI: 10.1038/s41467-019-13749-3

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