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arXiv · 1810.03972

Machine learning clustering technique applied to powder X-ray diffraction patterns to distinguish alloy substitutions

Keishu Utimula·Rutchapon Hunkao·Masao Yano·Hiroyuki Kimoto·Kenta Hongo·Shogo Kawaguchi·Sujin Suwanna·Ryo Maezono
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Abstract

We applied the clustering technique using DTW (dynamic time wrapping) analysis to XRD (X-ray diffraction) spectrum patterns in order to identify the microscopic structures of substituents introduced in the main phase of magnetic alloys. The clustering is found to perform well to identify the concentrations of the substituents with successful rates (around 90%). The sufficient performance is attributed to the nature of DTW processing to filter out irrelevant informations such as the peak intensities (due to the incontrollability of diffraction conditions in polycrystalline samples) and the uniform shift of peak positions (due to the thermal expansions of lattices).

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Keishu Utimula, Rutchapon Hunkao, Masao Yano, Hiroyuki Kimoto, Kenta Hongo, Shogo Kawaguchi, Sujin Suwanna, Ryo Maezono. 2019-03-28. Machine learning clustering technique applied to powder X-ray diffraction patterns to distinguish alloy substitutions. https://doi.org/10.1002/adts.202000039

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