ChemGraph-XANES: An Agentic Framework for XANES Simulation and Curation

Computational X-ray absorption near-edge structure (XANES) is widely used to interpret local coordination environments, oxidation states, and electronic structure in chemically complex systems.

In practice, routine computational XANES at scale is often constrained by workflow complexity rather than by the simulation method.

We present ChemGraph-XANES, a large-language-model (LLM)-based agentic framework for XANES simulation and analysis that combines retrieval-augmented generation (RAG)-assisted parameter selection from documentation, schema-constrained tool execution, deterministic FDMNES input generation, and provenance-aware data curation.

The framework supports both direct scripted execution and natural-language orchestration, with both modes routed through a deterministic backend for structure handling, absorber and edge specification, input generation, execution, spectral extraction, and post-processing.

We demonstrate three proof-of-capability use cases: RAG-assisted selection and propagation of FDMNES input parameters, structure-file-based execution, and chemistry-level natural-language specification of absorber and composition requests.

In a recorded trace, a simulation parameter is retrieved from the FDMNES manual by the RAG-enabled agent and propagated into a schema-validated tool call, illustrating traceable parameter selection.

We further show that the same execution pathway supports both explicit local structures and chemistry-level user inputs.

Because XANES calculations are independent once inputs are defined, ChemGraph-XANES is designed to support task-parallel execution and the creation of structure-linked XANES collections.

ChemGraph-XANES therefore serves as a practical agentic framework for computational spectroscopy and data generation, emphasizing constrained orchestration, reproducibility, and traceable outputs.