![]() 2 θ in reciprocal space and thereafter backward-Fourier-transformed into the electron density in real space, and thereby structural information of interest can be extracted from the XRD pattern. Within the framework of theoretical crystallography-based powder XRD analysis, the XRD pattern is interpreted as discrete intensity data vs. Here we propose providing lay persons who are not experts with a facile, prompt protocol for the quantitative identification of constituent phases in unknown multiphase mixtures.ĭeep-learning technologies have achieved a respected position in the materials research community 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 and could make it possible to accomplish a dream protocol that would enable instantaneous phase identification of samples of unknown mixtures. Despite the existence of a promising level of expertise, the prompt identification of constituent phases from an intricate multiphase mixture would be complicated when using conventional rule-based data analysis tools such as commercially available computational software packages 7, 8, 9. It would be arduous, however, for even a well-trained expert with the advantage of well-established computational tools to complete both the constituent phase identification and the ensuing phase-fraction estimation for a sample consisting of a grungy, multiphase mixture. One of the most frequently faced situations in the process of materials discovery based on the powder XRD technique involves the identification and quantification of unknown multiphase compounds. These materials include phosphors for solid-state lighting 1, 2, 3 and cathodes for rechargeable batteries 4, 5, 6. We have recently discovered many novel inorganic functional materials by employing powder X-ray diffraction (XRD) analysis. ![]()
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