aiqbench analysis
PhaseMatcher applies an autoregressive machine learning framework to solve the complex problem of phase identification in powder X-ray diffraction, addressing errors caused by peak overlap and inaccurate contribution estimation. By integrating physics-guided spectral decomposition with a dynamic residual update mechanism, the model iteratively refines its predictions to achieve more accurate complete-set identification than traditional scalar subtraction methods. This work represents a significant advancement in applying ML techniques to materials science data processing, specifically enhancing the reliability of structural analysis in noisy environments.
PhaseMatcher: Autoregressive Phase-Set Identification with Spectral Decomposition
arXiv:2610.05844v1 Announce Type: cross Abstract: Recovering complete phase sets from powder X-ray diffraction (PXRD) is challenging when weak-phase peaks overlap stronger signals. A natural strategy is to identify phases iteratively, removing the contribution of each identified phase from the observed pattern before predicting the next. However, even after a phase is correctly identified, misestimating its contribution can distort the residual and cause subsequent errors. We introduce PhaseMatcher, an autoregressive framework for complete phase-set identification with physics-guided spectral decomposition. After each phase prediction, PhaseMatcher re-estimates the contributions of all selected phases and the residual from the original observation and all selected reference patterns, accounting for physically plausible variation between reference patterns and the corresponding phase contributions in the observation. The resulting residual guides subsequent phase identification, while a separate stopping module determines when the phase set is complete. On synthetic mixtures and controlled mixtures constructed from measured single-phase patterns, PhaseMatcher improves complete-set identification over the evaluated baselines. On PhaseMix-135K, it also estimates contributions and residuals more accurately than scalar subtraction.
Abstract:Recovering complete phase sets from powder X-ray diffraction (PXRD) is challenging when weak-phase peaks overlap stronger signals. A natural strategy is to identify phases iteratively, removing the contribution of each identified phase from the observed pattern before predicting the next. However, even after a phase is correctly identified, misestimating its contribution can distort the residual and cause subsequent errors. We introduce PhaseMatcher, an autoregressive framework for complete phase-set identification with physics-guided spectral decomposition. After each phase prediction, PhaseMatcher re-estimates the contributions of all selected phases and the residual from the original observation and all selected reference patterns, accounting for physically plausible variation between reference patterns and the corresponding phase contributions in the observation. The resulting residual guides subsequent phase identification, while a separate stopping module determines when the phase set is complete. On synthetic mixtures and controlled mixtures constructed from measured single-phase patterns, PhaseMatcher improves complete-set identification over the evaluated baselines. On PhaseMix-135K, it also estimates contributions and residuals more accurately than scalar subtraction.
Computational Engineering, Finance, and Science (cs.CE); Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG)
arXiv:2610.05844 [cs.CE]
(or arXiv:2610.05844v1 [cs.CE] for this version)
https://doi.org/10.48550/arXiv.2610.05844 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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