ChemGraph-XANES：用于XANES模拟与分析的代理框架
ChemGraph-XANES: An Agentic Framework for XANES Simulation and Curation
AI总结：
本文提出ChemGraph-XANES框架，结合自然语言任务描述与结构获取，实现XANES模拟与分析的自动化，支持高通量计算与机器学习应用。
摘要：
计算X射线吸收近边结构（XANES）广泛用于探测复杂化学系统中的局部配位环境、氧化态和电子结构。然而，大规模应用XANES计算受限于工作流程的复杂性而非模拟方法本身。为此，我们提出了ChemGraph-XANES，一个代理框架，统一了自然语言任务规范、结构获取、FDMNES输入生成、任务并行执行、光谱归一化和基于溯源的数据整理。该框架基于ASE、FDMNES、Parsl以及基于LangGraph/LangChain的工具接口，将XANES工作流程操作暴露为类型化的Python工具，可由大型语言模型（LLM）代理编排。在多代理模式下，一个检索增强的专家代理咨询FDMNES手册以确定参数选择，而执行代理将用户请求转化为结构化工具调用。我们展示了基于文档的参数检索，并证明了相同的工作流程支持显式结构文件输入和化学层面的自然语言请求。由于独立的XANES计算本质上是任务并行的，该框架非常适合在高性能计算（HPC）系统上进行高通量部署，从而实现可扩展的XANES数据库生成，用于后续分析和机器学习应用。ChemGraph-XANES因此为基于物理的XANES模拟、光谱整理和代理兼容的计算光谱学提供了可重复和可扩展的工作流程层。
英文摘要：
Computational X-ray absorption near-edge structure (XANES) is widely used to interpret local coordination environments, oxidation states, and electronic structure, but large computational campaigns are often limited by workflow complexity. We present ChemGraph-XANES, a large language model (LLM)-based agentic framework that combines documentation-grounded parameter retrieval via retrieval-augmented generation (RAG), schema-constrained tool execution, deterministic FDMNES input generation, Parsl-backed execution, and provenance-aware data curation. Scripted and natural-language interfaces share a common scientific backend for structure handling, parameterization, execution, spectral extraction, and optional post-processing. We evaluate three workflow modes: documentation-grounded parameter propagation, structure-file-based execution, and composition-based execution from a chemistry-level request. Repeated trials yielded end-to-end completion in 10/10 composition-based runs, 10/10 structure-file-based runs, and 9/10 documentation-grounded RAG runs. In every RAG run, the energy-grid specification retrieved from the FDMNES manual was correctly propagated, with the single end-to-end failure occurring downstream during multi-structure handling. In a separate task-parallel demonstration, the framework retrieved 21 TiO$_2$ structures from the Materials Project and submitted one FDMNES calculation per structure. All calculations completed successfully, with Parsl distributing the independent tasks across the user-configured worker pool. Together, these results show that ChemGraph-XANES provides a constrained and reproducible orchestration layer for computational spectroscopy, supporting consistent execution of representative tasks, documentation-linked parameter selection, and task-parallel generation of structure-linked XANES collections.
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