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This article introduces the ChemGraph-XANES agent framework, which integrates natural language task description, structure acquisition, FDMNES simulation, and spectral analysis, supporting high-throughput XANES database generation and machine learning applications.
Section 01
This article introduces the ChemGraph-XANES agent framework, which integrates natural language task description, structure acquisition, FDMNES simulation, and spectral analysis. It addresses the bottleneck of complex computational XANES workflows and supports high-throughput XANES database generation and machine learning applications.
Section 02
XANES is a key material characterization technique that can obtain information such as local coordination environments. However, experiments rely on synchrotron radiation sources and have harsh conditions. Computational XANES is an alternative, but its workflow is complex: it requires structure preparation and acquisition, simulation parameter setting, computation execution and error handling, post-processing and spectral analysis, which limits its popularization.
Section 03
The platform is built on ASE, FDMNES, Parsl, and LangGraph/LangChain, and adopts multi-agent collaboration: expert agents provide parameter suggestions via RAG, execution agents orchestrate tool calls and handle exceptions, and curation agents manage results and metadata.
Section 04
Section 05
The platform supports large-scale parallel computing to generate XANES databases, which can be used for material screening (pre-experiment candidate screening), machine learning training data (spectral-structure mapping), and automated analysis workflows (end-to-end experimental spectral inversion).
Section 06
The platform fully records computational steps, parameters, and workflows to ensure result reproducibility; standardized processing eliminates method differences, promoting community-shared databases and collaborative research.
Section 07
ChemGraph-XANES lowers computational barriers, improves efficiency, and enhances reproducibility. It represents the trend of integration between computational spectroscopy and AI. In the future, it is expected to promote the emergence of agent platforms in more fields and transform scientific research paradigms.
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