利用干湿智能体循环诊断并缩小粉末X射线衍射中的模拟到真实差距
Diagnosing and narrowing the simulation-to-real gap in powder X-ray diffraction with a wet-dry agentic loop
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中文总结 AI 辅助
该研究针对粉末X射线衍射的模拟到真实差距，提出整合实测图谱微调等技术的Xtalyst智能体系统，通过干湿循环优化，提升了物相分析的准确性与覆盖度。
中文摘要 AI 辅助
粉末X射线衍射（PXRD）是晶体物质的常规探测手段，但随着实验室采集自动化，其分析成为限速步骤。深度学习分析器在模拟图谱上表现优异，在实测图谱上性能下降。这种模拟到真实的差距是结构性的而非叠加性的：合成去噪对真实图谱无明显提升，而修正微小的峰位漂移可使中位数检索相关性翻倍以上。实测图谱微调、峰对齐重排序和重新校准可缩小剩余差距，恢复合成锚点丢失的覆盖度。Xtalyst将这些技术整合为智能体编排系统，涵盖物相鉴定、精修和校准性质预测。在保留的测试集（n=534）上，各模块在两个划分集上的测量结果均复现其开发发现——包括合成锚点覆盖不足，其幅度在两个数据集间存在差异；同时，保留的精修收敛并保持对称性，虽未达到轮廓质量拟合，且在衍射仪上，其干湿推荐-重扫-重分析循环将盲测硅标准转为门控PASS，并改变了多金属合金上解析的次要物相。
英文摘要
Powder X-ray diffraction (PXRD) is the routine probe of crystalline matter, yet its analysis is the rate-limiting step as laboratories automate acquisition. Deep-learning analyzers excel on simulated patterns and degrade on measured ones. This simulation-to-real gap is structural, not additive: synthetic denoising gives no measurable lift on real spectra, whereas correcting a small peak-position drift more than doubles median retrieval correlation. Real-spectrum fine-tuning, peak-aligned reranking, and recalibration narrow what remains and restore the coverage synthetic anchors lose. Xtalyst integrates these in an agent-orchestrated system spanning phase identification, refinement, and calibrated property prediction. On a frozen held-out partition (n=534) each module measured on both splits reproduces its development finding -- including the synthetic-anchor under-coverage, whose magnitude differs between the two pools -- while held-out refinement converges and preserves symmetry without reaching profile-quality fits, and on a diffractometer its wet-dry recommend-rescan-reanalyze loop flips a blinded silicon standard to a gated PASS and changes which minor phase is resolved on a multi-metal alloy.
