MatDiffract：用于X射线粉末衍射的材料信息自动分析平台
MatDiffract: A Material-Informed Automated Analysis Platform for X-ray Powder Diffraction
AI总结：
MatDiffract平台致力于解决XRPD数据自动解释问题，基于Atomly数据库，通过构建模拟衍射数据库等方法，实现了对XRPD的高通量表征，在单相和多相混合物测试中取得高准确率，能快速输出完整晶体学结果，支持新材料系统扩展。
AI中文摘要：
高通量实验和自动驾驶实验室正在加速材料发现，但X射线粉末衍射（XRPD）数据的自动解释仍是关键限速步骤。传统搜索匹配工作流程依赖专家人工干预，纯数据驱动机器学习方法通用性有限且缺乏晶体学解释性。本文提出MatDiffract，基于第一性原理密度泛函理论衍生的无机晶体结构数据库Atomly构建，通过构建扰动增强模拟衍射数据库等一系列操作，实现对XRPD的高通量表征。在875个单相实验模式上进行基准测试，自动精炼后平台实现了91.3%的Top-1和97.2%的Top-10识别准确率等。MatDiffract能在数十秒内输出完整晶体学结果，其模块化架构支持向新材料系统无缝扩展，为自主材料发现和高通量材料开发提供端到端解决方案。
英文摘要：
High-throughput experimentation and self-driving laboratories are drastically accelerating materials discovery, yet automated interpretation of X-ray powder diffraction (XRPD) data remains a critical rate-limiting step. Conventional search-match workflows rely heavily on expert manual intervention, while pure data-driven machine learning approaches suffer from limited generalizability across chemical systems and lack rigorous crystallographic interpretability. Here we present MatDiffract, a material-informed automated analysis platform for high-throughput XRPD characterization. Built on a first-principles density functional theory (DFT)-derived inorganic crystal structure database, Atomly, MatDiffract constructs a perturbation-augmented simulated diffraction database, embeds multi-scale diffraction features into indexable vectors, and integrates hierarchical vector retrieval with full-pattern fitting Rietveld refinement and quantitative phase fitting. Benchmarked on 875 single-phase experimental patterns, the platform achieves 91.3% Top-1 and 97.2% Top-10 identification accuracy after automated refinement. For binary and ternary multiphase mixtures, it delivers 85.0% and 70.0% Top-1 accuracy with mass fraction mean absolute errors as low as 1.2% and 1.8%, respectively. Beyond mere phase labeling, MatDiffract outputs full crystallographic results including refined structural models, fitted profiles, and quantitative compositions within tens of seconds per sample. Its modular vector-based architecture supports seamless incremental expansion to new material systems, providing an end-to-end solution to close the characterization throughput gap for autonomous materials discovery and high-throughput materials development.
