用多智能体模拟专家分析红外光谱，自动推断分子结构
IR-Agent: Expert-Inspired LLM Agents for Structure Elucidation from Infrared Spectra
- 设计多个专精于不同解析任务的智能体协同工作
- 在真实红外光谱上提升结构推断准确率，适应多种化学知识
- 适合需要精准物性分析的化学研究者和工业质检人员
光谱分析为未知物质结构解析提供关键线索。红外光谱（IR）因高可及性和低成本，在实验室中应用广泛。然而，现有方法难以体现专家分析流程，且缺乏对多样化化学知识的灵活整合能力，难以应对真实场景需求。本文提出IR-Agent，一种用于从红外光谱中进行分子结构解析的新型多智能体框架。该框架旨在模拟专家驱动的红外分析过程，具有内在可扩展性。每个智能体专注于红外解析的特定方面，其互补角色实现综合推理，从而提升整体结构解析准确性。通过大量实验验证，IR-Agent不仅在实验红外光谱上优于基线模型，还展现出对多种化学信息形式的强大适应能力。
原文摘要 · Abstract (English)
Spectral analysis provides crucial clues for the elucidation of unknown materials. Among various techniques, infrared spectroscopy (IR) plays an important role in laboratory settings due to its high accessibility and low cost. However, existing approaches often fail to reflect expert analytical processes and lack flexibility in incorporating diverse types of chemical knowledge, which is essential in real-world analytical scenarios. In this paper, we propose IR-Agent, a novel multi-agent framework for molecular structure elucidation from IR spectra. The framework is designed to emulate expert-driven IR analysis procedures and is inherently extensible. Each agent specializes in a specific aspect of IR interpretation, and their complementary roles enable integrated reasoning, thereby improving the overall accuracy of structure elucidation. Through extensive experiments, we demonstrate that IR-Agent not only improves baseline performance on experimental IR spectra but also shows strong adaptability to various forms of chemical information.
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