Scalable machine learning framework for multiphase identification from powder X-ray diffraction
The paper introduces GALAXI, a scalable machine learning framework that decouples multiphase X-ray diffraction identification into independent binary classifiers and Rietveld refinement, achieving high accuracy and robustness against experimental artifacts while enabling the use of massive crystallographic databases without retraining.