Powder X-ray diffraction (XRD) is a foundational technique for characterizing crystalline materials. However, the reliable interpretation of XRD patterns, particularly in multiphase systems, remains a manual and expertise-demanding task. As a characterization method that only provides structural information, multiple reference phases can often be fit to a single pattern, leading to potential misinterpretation when alternative solutions are overlooked. To ease humans' efforts and address the challenge, we introduce Dara (Data-driven Automated Rietveld Analysis), a framework designed to automate the robust identification and refinement of multiple phases from powder XRD data. Dara performs an exhaustive tree search over all plausible phase combinations within a given chemical space and validates each hypothesis using a robust Rietveld refinement routine (BGMN). Key features include structural database filtering, automatic clustering of isostructural phases during tree expansion, peak-matching-based scoring to identify promising phases for refinement. When ambiguity exists, Dara generates multiple hypothesis which can then be decided between by human experts or with further characteriztion tools. By enhancing the reliability and accuracy of phase identification, Dara enables scalable analysis of realistic complex XRD patterns and provides a foundation for integration into multimodal characterization workflows, moving toward fully self-driving materials discovery.

翻译：粉末X射线衍射（XRD）是表征晶体材料的基础技术。然而，XRD图谱的可靠解析，尤其是在多相体系中，仍是一项依赖人工且需要专业知识的任务。作为一种仅提供结构信息的表征方法，单个衍射图谱常可匹配多个参考物相，当忽略替代性解决方案时可能导致误判。为减轻人工负担并应对这一挑战，我们提出了Dara（数据驱动的自动Rietveld分析框架），该框架旨在实现粉末XRD数据中多物相的鲁棒性自动识别与精修。Dara在给定化学空间内对所有可能的物相组合进行穷举树搜索，并通过鲁棒的Rietveld精修程序（BGMN）验证每个假设。其核心功能包括：结构数据库过滤、树扩展过程中同构物相的自动聚类、基于峰匹配的评分机制以筛选适合精修的潜力物相。当存在歧义时，Dara会生成多个假设，供领域专家或借助进一步表征工具进行决策。通过提升物相识别的可靠性与准确性，Dara实现了对实际复杂XRD图谱的可扩展分析，并为融入多模态表征工作流程奠定了基础，推动材料发现向全自动化方向迈进。