发表机构
University of California, Los Angeles; University of California, San Diego（加州大学洛杉矶分校; 加州大学圣地亚哥分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
GALAXI通过独立一对多分类器缩小搜索空间并结合Rietveld精修，实现多相XRD识别，微F1达0.935，可扩展至大型参考库。
AI 中文摘要
X射线衍射（XRD）是合成后鉴定晶相的主要工具，但自动物相鉴定仍然具有挑战性，特别是对于具有重叠峰和实验伪影的多相样品。虽然已提出深度学习方法以改进经典搜索匹配算法，但大多数方法将物相鉴定表述为单一封闭集分类问题，要求一个共享模型区分所有候选相。这里我们介绍GALAXI，它反而将鉴定任务解耦为独立的一对多二元分类器，每个分类器专门识别单一相。这些预训练分类器首先将搜索空间缩小到少量合理相，然后通过Rietveld精修评估这些相，以识别最能解释完整衍射图的相组合。在一组精选的实验图谱上，GALAXI以0.935的微F1分数识别出正确相，优于经典搜索匹配和先前的深度学习模型。该方法对常见实验伪影保持稳健，包括低杂质相分数、小晶粒尺寸、峰位移、样品位移和织构，并且在应用于固态反应的时间分辨原位XRD数据时表现良好。此外，由于相特定模型是独立的，GALAXI可以扩展到大型参考库而无需重新训练现有模型。这种模块化架构使我们能够为来自晶体学开放数据库的64,594个结构训练分类器，并通过公共网页界面在此https URL部署它们。
英文摘要
X-ray diffraction (XRD) is the primary tool for identifying crystalline phases following synthesis, but automated phase identification remains challenging, particularly for multiphase samples with overlapping peaks and experimental artifacts. While deep-learning methods have been proposed to improve upon classical search-match algorithms, most formulate phase identification as a single closed-set classification problem, requiring one shared model to discriminate among all candidate phases. Here we introduce GALAXI, which instead decouples the identification task into independent one-versus-all binary classifiers that each specialize in recognizing a single phase. These pre-trained classifiers first narrow the search space to a small set of plausible phases, which are then evaluated through Rietveld refinement to identify the combination of phases that best explains the full diffraction pattern. On a curated set of experimental patterns, GALAXI identifies the correct phases with a micro-F1 score of 0.935, outperforming classical search-match and prior deep-learning models. The method remains robust to common experimental artifacts, including low impurity phase fractions, small crystallite size, peak shifts, sample displacement, and texture, and performs well when applied to time-resolved in-situ XRD data from solid-state reactions. Moreover, because the phase-specific models are independent, GALAXI can expand to large reference libraries without retraining existing models. This modular architecture enables us to train classifiers for 64,594 structures from the Crystallography Open Database and deploy them through a public web interface at https://galaxi-xrd.com.
