ChemGraph-XANES: An Agentic Framework for XANES Simulation and Analysis
- URL: http://arxiv.org/abs/2604.16205v1
- Date: Fri, 17 Apr 2026 16:15:19 GMT
- Title: ChemGraph-XANES: An Agentic Framework for XANES Simulation and Analysis
- Abstract summary: ChemGraph-XANES is an agentic framework for automated XANES simulation and analysis.<n>It unifies natural-language task specification, structure acquisition, FDMNES input generation, spectral normalization, and data curation.
- Score: 4.554542395453299
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- El Agente Potente: High-Throughput Agentic Atomistic Simulations [4.268668578817565]
Foundational machine-learning interatomic potentials (MLIPs) are transforming atomistic simulations by achieving near-ab initio accuracy across large chemical spaces at a fraction of the computational cost.<n>We introduce El Agente Potente, an agentic system that combines typed execution graphs with a complementary coding mode for MLIPs-driven atomistic simulations.
arXiv Detail & Related papers (2026-09-13T23:20:21Z) - El Agente Gráfico: Structured Execution Graphs for Scientific Agents [7.47895130442454]
We present El Agente Grfico, a single-agent framework that embeds large language models (LLMs)-driven decision-making within a type-safe execution environment.<n>Central to our approach is a structured abstraction of scientific concepts and an object-graph mapper that represents computational state as typed Python objects.<n>We evaluate the system by developing an automated benchmarking framework across a suite of university-level quantum chemistry tasks.
arXiv Detail & Related papers (2026-02-19T23:47:05Z) - Large Language Model Agent for User-friendly Chemical Process Simulations [0.0]
A large language model (LLM) agent is integrated with AVEVA Process Model Protocol (MCP), allowing natural language simulations.<n>Two case studies assess the framework across different task complexities and interaction modes.<n>The framework benefits both educational purposes, by translating technical concepts and demonstrating, and experienced practitioners by automating data extraction, speeding routine tasks, and supporting.<n>While current limitations such as oversimplification, calculation errors, and technical hiccups mean expert oversight is still needed, the framework suggests LLM-based agents can become valuable collaborators.
arXiv Detail & Related papers (2026-01-15T12:18:45Z) - An Agentic Framework for Autonomous Materials Computation [70.24472585135929]
Large Language Models (LLMs) have emerged as powerful tools for accelerating scientific discovery.<n>Recent advances integrate LLMs into agentic frameworks, enabling retrieval, reasoning, and tool use for complex scientific experiments.<n>Here, we present a domain-specialized agent designed for reliable automation of first-principles materials computations.
arXiv Detail & Related papers (2025-12-22T15:03:57Z) - Code-in-the-Loop Forensics: Agentic Tool Use for Image Forgery Detection [59.04089915447622]
ForenAgent is an interactive IFD framework that enables MLLMs to autonomously generate, execute, and refine Python-based low-level tools around the detection objective.<n>Inspired by human reasoning, we design a dynamic reasoning loop comprising global perception, local focusing, iterative probing, and holistic adjudication.<n>Experiments show that ForenAgent exhibits emergent tool-use competence and reflective reasoning on challenging IFD tasks.
arXiv Detail & Related papers (2025-12-18T08:38:44Z) - Sample-Efficient Online Learning in LM Agents via Hindsight Trajectory Rewriting [92.57796055887995]
We introduce ECHO, a prompting framework that adapts hindsight experience replay from reinforcement learning for language model agents.<n> ECHO generates optimized trajectories for alternative goals that could have been achieved during failed attempts.<n>We evaluate ECHO on stateful versions of XMiniGrid, a text-based navigation and planning benchmark, and PeopleJoinQA, a collaborative information-gathering enterprise simulation.
arXiv Detail & Related papers (2025-10-11T18:11:09Z) - ChemActor: Enhancing Automated Extraction of Chemical Synthesis Actions with LLM-Generated Data [53.78763789036172]
We present ChemActor, a fully fine-tuned large language model (LLM) as a chemical executor to convert between unstructured experimental procedures and structured action sequences.<n>This framework integrates a data selection module that selects data based on distribution divergence, with a general-purpose LLM, to generate machine-executable actions from a single molecule input.<n>Experiments on reaction-to-description (R2D) and description-to-action (D2A) tasks demonstrate that ChemActor achieves state-of-the-art performance, outperforming the baseline model by 10%.
arXiv Detail & Related papers (2025-06-30T05:11:19Z) - Spectra-to-Structure and Structure-to-Spectra Inference Across the Periodic Table [49.65586812435899]
XAStruct is a learning-based system capable of both predicting XAS spectra from crystal structures and inferring local structural descriptors from XAS input.<n>XAStruct is trained on a large-scale dataset spanning over 70 elements across the periodic table.
arXiv Detail & Related papers (2025-06-13T15:58:05Z) - A new framework for X-ray absorption spectroscopy data analysis based on machine learning: XASDAML [3.26781102547109]
XASDAML is a flexible, machine learning based framework that integrates the entire data-processing workflow.<n>It supports comprehensive statistical analysis, leveraging methods such as principal component analysis and clustering.<n>The versatility and effectiveness of XASDAML are exemplified by its application to a copper dataset.
arXiv Detail & Related papers (2025-02-23T17:50:04Z) - SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines.
This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
arXiv Detail & Related papers (2021-12-22T14:45:37Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.