IR-Agent: Expert-Inspired LLM Agents for Structure Elucidation from Infrared Spectra
Heewoong Noh, Namkyeong Lee, Gyoung S. Na, Kibum Kim, Chanyoung Park
Abstract
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. The source code for IR-Agent is available at https://github.com/HeewoongNoh/IR-Agent .
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 7a7a7149-13f9-4af3-845b-de19baaca16fBuilds on5
- Translation between Molecules and Natural LanguageCarl Edwards, Tuan Manh Lai, Kevin Ros, Garrett Honke et al.EMNLP 2022 · 112 citations
- Conversational Drug Editing Using Retrieval and Domain FeedbackShengchao Liu, Jiongxiao Wang, Yijin Yang, Chengpeng Wang et al.ICLR 2024 · 48 citations
- Conditional Graph Information Bottleneck for Molecular Relational LearningNamkyeong Lee, Dongmin Hyun, Gyoung S. Na, Sungwon Kim et al.ICML 2023 · 42 citations
- Density of States Prediction of Crystalline Materials via Prompt-guided Multi-Modal TransformerNamkyeong Lee, Heewoong Noh, Sungwon Kim, Dongmin Hyun et al.NeurIPS 2023 · 12 citations
- BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation ExperimentsYusuf H. Roohani, Andrew H. Lee, Qian Huang, Jian Vora et al.ICLR 2025
Related papers
- SpectraLLM: Uncovering the Ability of LLMs for Molecule Structure Elucidation from Multi-SpectraYunyue Su, Jiahui Chen, Zao Jiang, Zhenyi Zhong et al.ICLR 2026 · 1 citation
- MM-Spectrum: Multimodal Multi-spectral Molecular Structural Elucidation with a Stable MoE FrameworkHaitao YU, Nan Min, Zheng Fang, Hongyu Zhan et al.ICML 2026
- Boosting LLM's Molecular Structure Elucidation with Knowledge Enhanced Tree Search ReasoningXiang Zhuang, Bin Wu, Jiyu Cui, Kehua Feng et al.ACL 2025 · 1 citation
- ChemAgent: Self-updating Memories in Large Language Models Improves Chemical ReasoningXiangru Tang, Tianyu Hu, Muyang Ye, Yanjun Shao et al.ICLR 2025
- SVAgent: Storyline-guided Long Video Understanding via Cross-Modal Multi-Agent CollaborationZhongyu Yang, Zuhao Yang, Shuo Zhan, Tan Yue et al.CVPR 2026 · 5 citations
