PhaseMatcher: Autoregressive Phase-Set Identification with Spectral Decomposition
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.
Related
- Identification and Structural Decomposition of Hidden Defect Configurations: A Case Study of Charged Oxygen Divacancies in HfO2
- Directional memory of early spectral selection during spinodal decomposition in finite systems
- Fourier Finite-Difference method and Proper Orthogonal Decomposition for quick spectral study of 2D periodic photonic structures
- Block-modular grey-box identification of a short-stroke seawater-pump power take-off for wave energy: loss decomposition and multi-regime validation
- Spectral origin of the topological gap exponent d + η: mechanism, kernel, decomposition, and scope
BibTeX
@misc{peng2026phasematcher,
title = {{PhaseMatcher: Autoregressive Phase-Set Identification with Spectral Decomposition}},
author = {Zhonglong Peng and Qiuliang Liu and Chang Chen and Geng Zhong and Qi Li and Lihong Wang and Lan Jiang and Shifeng Jin},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.05844},
eprint = {2610.05844},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.05844},
}
RIS
TY - UNPB
TI - PhaseMatcher: Autoregressive Phase-Set Identification with Spectral Decomposition
AU - Zhonglong Peng
AU - Qiuliang Liu
AU - Chang Chen
AU - Geng Zhong
AU - Qi Li
AU - Lihong Wang
AU - Lan Jiang
AU - Shifeng Jin
PY - 2026
DA - 2026/10/05
PB - arXiv
DO - 10.48550/arxiv.2610.05844
UR - https://arxiv.org/abs/2610.05844
AB - 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.
ER -