PhaseMatcher：基于谱分解的自回归相集识别

中文摘要 AI 辅助

从粉末X射线衍射（PXRD）中恢复完整的相集在弱相峰与较强信号重叠时具有挑战性。一种自然的策略是迭代识别相，在预测下一个相之前，从观测模式中去除每个已识别相的贡献。然而，即使在一个相被正确识别后，对其贡献的错误估计也会扭曲残差并导致后续错误。我们引入了PhaseMatcher，一个具有物理引导谱分解的完整相集识别自回归框架。在每次相预测后，PhaseMatcher从原始观测和所有选定参考模式中重新估计所有选定相的贡献和残差，考虑参考模式与观测中相应相贡献之间物理上合理的变异。由此产生的残差指导后续相识别，而一个单独的停止模块确定相集何时完整。在由测量的单相模式构建的合成混合物和受控混合物上，PhaseMatcher在完整集识别上优于评估的基线。在PhaseMix-135K上，它也比标量减法更准确地估计贡献和残差。

英文摘要

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.

发表机构

• Institute of Physics, Chinese Academy of Sciences（中国科学院物理研究所）
• University of the Chinese Academy of Sciences（中国科学院大学）
• Xi’an Jiaotong University（西安交通大学）
• Beijing University of Posts and Telecommunications（北京邮电大学）

机构由 AI 辅助整理，请以论文原文为准。

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51 pages