Exploring Supervised Machine Learning for Multi-Phase Identification and Quantification from Powder X-Ray Diffraction Spectra. (arXiv:2211.08591v1 [cond-mat.mtrl-sci])

Powder X-ray diffraction analysis is a critical component of materials
characterization methodologies. Discerning characteristic Bragg intensity peaks
and assigning them to known crystalline phases is the first qualitative step of
evaluating diffraction spectra. Subsequent to phase identification, Rietveld
refinement may be employed to extract the abundance of quantitative,
material-specific parameters hidden within powder data. These characterization
procedures are yet time-consuming and inhibit efficiency in materials science
workflows. The ever-increasing popularity and propulsion of data science
techniques has provided an obvious …

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