arXiv:2604.16205v2 Announce Type: replace-cross Abstract: 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, s
The proliferation of advanced LLMs and agentic frameworks is enabling the automation of complex scientific simulations and data curation, which was previously a bottleneck.
This development automates sophisticated materials science research, accelerating discovery and enabling the routine analysis of complex chemical systems at scale, which has broad industrial implications.
Previously manual or semi-manual XANES simulation and analysis workflows can now be significantly automated and scaled through an LLM-based agentic framework.
- · Materials science researchers
- · Pharmaceutical industry
- · Chemical engineering sector
- · AI software developers
Computational materials discovery becomes significantly faster and more accessible.
New materials with tailored properties are developed more rapidly, impacting various industries from energy to electronics.
The increased pace of discovery could lead to a competitive advantage for nations and companies leveraging these AI tools in R&D.
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Read at arXiv cs.AI