AI 中文总结
研究针对X射线衍射分析转化晶体结构需专业知识的问题，提出AGAPI-XRD混合框架，整合多种方法，经实验评估和基准测试，该框架能有效返回晶格参数、确定候选结构，推动了端到端自动晶体结构测定。
AI 中文摘要
X射线衍射（XRD）是结构材料表征的基础，但将原始粉末图谱转化为精确的晶体结构仍需要相当多的领域专业知识。我们提出了AGAPI-XRD，这是一个混合框架，它整合了DiffractGPT生成结构预测、针对JARVIS-DFT和COD的数据库模式匹配，以及通过统一API进行的自动Rietveld精修和ALIGNN-FF弛豫。首先，我们用AGAPI-XRD管道评估了RRUFF数据库中多种通过粉末X射线衍射实验表征的矿物晶体结构。接着，我们用Alexandria PBE-hull数据集的一个子集和具有已知晶格参数的RRUFF矿物子集对AGAPI-XRD管道的晶格参数预测保真度进行了基准测试。AGAPI-XRD为79.7%的RRUFF基准矿物和94.8%-98.1%的Alexandria子集返回了有效的晶格参数，同时为93.8%的RRUFF矿物确定了候选结构。对于这个基准测试，模式匹配对已知相的准确性最高，而DiffractGPT将结构生成扩展到了现有数据库中没有的复杂材料。总之，AGAPI-XRD推动了从粉末XRD数据进行可访问的端到端自动晶体结构测定。
英文摘要
X-ray diffraction (XRD) is fundamental to structural materials characterization, yet transforming a raw powder pattern into a refined crystal structure still demands considerable domain expertise. We present AGAPI-XRD, a hybrid framework integrating DiffractGPT generative structure prediction, database pattern matching against JARVIS-DFT and COD, and automated Rietveld refinement and ALIGNN-FF relaxation through a unified API at https://atomgpt.org/xrd. First, we used the AGAPI-XRD pipeline to evaluate the crystal structure of a variety of minerals in the RRUFF database that were experimentally characterized using powder x-ray diffraction. Next, we benchmarked the lattice parameter prediction fidelity of the AGAPI-XRD pipeline using a subset of the Alexandria PBE-hull dataset and the subset of RRUFF minerals that have known lattice parameters. AGAPI-XRD returns valid lattice parameters for 79.7\% of the RRUFF benchmark minerals and for 94.8--98.1\% of the Alexandria subset, while identifying a candidate structure for 93.8\% of RRUFF minerals. For this benchmark, pattern matching delivers the highest accuracy for known phases, while DiffractGPT extends structure generation to complex materials absent from existing databases. Together, AGAPI-XRD advances accessible, end-to-end automated crystal structure determination from powder XRD data.
