PhaseMatcher: Autoregressive Prediction of Complete Phase Sets from Powder X-Ray Diffraction

Recovering the complete set of crystalline phases in a mixture from its powder X-ray diffraction (PXRD) pattern is a central task in automated materials characterization. Peak overlap, abundance imbalance, and measurement-induced spectral variation obscure weak-phase evidence and make multiphase identification against large phase libraries difficult. We formulate multiphase PXRD analysis as exact-set prediction with unknown cardinality. PhaseMatcher predicts phases autoregressively from a large reference library and learns when the selected set is complete. After each selection, a physics-guided spectral decomposition module uses the original observation and all selected reference patterns to reconstruct the selected-phase contribution patterns and the residual spectrum. The normalized residual then guides the next-phase prediction, coupling phase identification with spectral decomposition. We construct PhaseMix-135K, a noisy synthetic multiphase PXRD dataset with physically motivated pattern variations, and a controlled mixture dataset from measured RRUFF single-phase patterns. PhaseMatcher achieves state-of-the-art exact-set recovery on the primary noisy benchmark and recovers complete phase sets on the controlled RRUFF dataset.