Machine‐Learning Clustering Technique Applied to Powder X‐Ray Diffraction Patterns to Distinguish Compositions of ThMn12‐Type Alloys. Issue 7 (5th June 2020) - Utimula, Keishu; Hunkao, Rutchapon; Yano, Masao; Kimoto, Hiroyuki; Hongo, Kenta; Kawaguchi, Shogo; Suwanna, Sujin; Maezono, Ryo
Abstract: A clustering technique is applied using dynamic‐time‐wrapping (DTW) analysis to X‐ray diffraction (XRD) spectrum patterns in order to identify the microscopic structures of substituents introduced into the main phase of magnetic alloys. The clustering technique is found to perform well, identifying the concentrations of the substituents with success rates of ≈90%. This level of performance is attributed to the capability of DTW processing to filter out irrelevant information such as the peak intensities (due to the uncontrollability of diffraction conditions in polycrystalline samples) and the uniform shift of peak positions (due to the thermal expansion of lattices). The established framework is not limited to the system treated in this work, but is widely applicable to systems the properties of which are to be tuned by atomic substitutions within a phase. The framework has a broader potential to predict properties such as magnetic moments, optical spectra etc.) from observed XRD patterns, by predicting such properties evaluated from predicted microscopic local structure. Abstract : A data clustering technique to sort X‐ray diffraction (XRD) peak patterns is found to be useful to distinguish doping concentrations of magnetic alloys when the "distance" between patterns is measured by the dynamic‐time‐wrapping (DTW) scheme. This framework is proved to achieve more than 90% success.