Computational X-ray absorption near-edge structure (XANES) is widely used to probe local coordination environments, oxidation states, and electronic structure in chemically complex systems. However, the use of computational XANES at scale is constrained more by workflow complexity than by the underlying simulation method itself. To address this challenge, we present ChemGraph-XANES, an agentic framework for automated XANES simulation and analysis that unifies natural-language task specification, structure acquisition, FDMNES input generation, task-parallel execution, spectral normalization, and provenance-aware data curation. Built on ASE, FDMNES, Parsl, and a LangGraph/LangChain-based tool interface, the framework exposes XANES workflow operations as typed Python tools that can be orchestrated by large language model (LLM) agents. In multi-agent mode, a retrieval-augmented expert agent consults the FDMNES manual to ground parameter selection, while executor agents translate user requests into structured tool calls. We demonstrate documentation-grounded parameter retrieval and show that the same workflow supports both explicit structure-file inputs and chemistry-level natural-language requests. Because independent XANES calculations are naturally task-parallel, the framework is well suited for high-throughput deployment on high-performance computing (HPC) systems, enabling scalable XANES database generation for downstream analysis and machine-learning applications. ChemGraph-XANES thus provides a reproducible and extensible workflow layer for physics-based XANES simulation, spectral curation, and agent-compatible computational spectroscopy.

翻译：计算X射线吸收近边结构（XANES）被广泛用于探测化学复杂体系中的局域配位环境、氧化态和电子结构。然而，计算XANES的大规模应用受到工作流复杂性的限制，而非底层模拟方法本身。为应对这一挑战，我们提出了ChemGraph-XANES，这是一个面向自动化XANES模拟与分析的多智能体框架，集成了自然语言任务规约、结构获取、FDMNES输入生成、任务并行执行、谱图归一化以及可追溯数据管理等功能。该框架基于ASE、FDMNES、Parsl以及LangGraph/LangChain工具接口构建，将XANES工作流操作封装为类型化的Python工具，可由大语言模型（LLM）智能体进行编排。在多智能体模式下，检索增强型专家智能体可查阅FDMNES手册以指导参数选择，而执行智能体则将用户请求转化为结构化工具调用。我们验证了基于文档的参数检索功能，并表明同一工作流既能处理显式结构文件输入，也能响应化学层面的自然语言请求。由于独立的XANES计算天然具有任务并行性，该框架特别适用于在高性能计算系统上开展高通量部署，从而支持可扩展的XANES数据库生成，为下游分析和机器学习应用提供支撑。因此，ChemGraph-XANES为基于物理的XANES模拟、谱图整理及智能体兼容的计算光谱学提供了一层可复现、可扩展的工作流框架。