AI生成晶体结构后，用物理约束算法稳定解析重叠峰，提升精修准确率。
AI-Driven Structure Refinement of X-ray Diffraction
- 将布拉格定律嵌入期望-最大化框架，实现全谱概率分解与迭代精修。
- 在硫酸铅和铽钡钴氧化物测试中，残差比FullProf和TOPAS更低。
- 适用于多相混合、无定形背景分离、考古样品等复杂实验场景。
人工智能可快速从X射线衍射（XRD）数据中提出候选相和结构，但这些假设在后续精修中常因峰强度难以稳定分配而失败，且衍射一致性约束较弱。本文提出全谱期望-最大化（WPEM）算法，将布拉格定律作为显式约束引入批处理期望-最大化框架，将完整衍射谱建模为概率混合密度，迭代推断组分化强度的同时保持峰位布拉格一致，生成连续且物理合理的强度表征，在严重重叠区域、混合辐射或多相体系下仍保持稳定。我们在标准参考谱（PbSO₄ 和 Tb₂BaCoO₅）上进行基准测试，发现其在匹配精修条件下获得的 $R_p/R_{wp}$ 低于广泛使用的 FullProf 与 TOPAS 软件。进一步验证了其在真实实验中的通用性：包括多相材料的相分辨分解、混合物组分定量恢复、半晶态系统中结晶峰与无定形背景分离、高通量原位晶格追踪、成分无序固溶体的自动化精修，以及来自同步辐射粉末XRD的复杂考古样品的定量相分辨分析。WPEM通过提供布拉格一致且带不确定性的强度分割结果，作为精修就绪接口，弥合了AI生成假设与衍射可接受结构精修之间的差距。
原文摘要 · Abstract (English)
Artificial intelligence can rapidly propose candidate phases and structures from X-ray diffraction (XRD), but these hypotheses often fail in downstream refinement because peak intensities cannot be stably assigned under severe overlap and diffraction consistency is enforced only weakly. Here we introduce the whole-pattern expectation--maximization (WPEM) algorithm, a physics-constrained whole-pattern decomposition and refinement workflow that turns Bragg's law into an explicit constraint within a batch expectation--maximization framework. WPEM models the full profile as a probabilistic mixture density and iteratively infers component-resolved intensities while keeping peak centres Bragg-consistent, producing a continuous, physically admissible intensity representation that remains stable in heavily overlapped regions and in the presence of mixed radiation or multiple phases. We benchmark WPEM on standard reference patterns (PbSO$_4$ and Tb$_2$BaCoO$_5$), where it yields lower $R_p/R_{wp}$ than widely used packages (FullProf and TOPAS) under matched refinement conditions. We further demonstrate generality across realistic experimental scenarios, including phase-resolved decomposition in multiphase materials, quantitative recovery of mixture compositions, separation of crystalline peaks from amorphous backgrounds in semicrystalline systems, high-throughput operando lattice tracking, automated refinement of compositionally disordered solid solutions, and quantitative phase-resolved analysis of complex archaeological samples from synchrotron powder XRD. By providing Bragg-consistent, uncertainty-aware intensity partitioning as a refinement-ready interface, WPEM closes the gap between AI-generated hypotheses and diffraction-admissible structure refinement on challenging XRD data.
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