IR-Agent: Expert-Inspired LLM Agents for Structure Elucidation from Infrared Spectra
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913142535094272 |
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| author | Noh, Heewoong Lee, Namkyeong Na, Gyoung S. Kim, Kibum Park, Chanyoung |
| author_facet | Noh, Heewoong Lee, Namkyeong Na, Gyoung S. Kim, Kibum Park, Chanyoung |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16112 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | IR-Agent: Expert-Inspired LLM Agents for Structure Elucidation from Infrared Spectra Noh, Heewoong Lee, Namkyeong Na, Gyoung S. Kim, Kibum Park, Chanyoung Artificial Intelligence 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. |
| title | IR-Agent: Expert-Inspired LLM Agents for Structure Elucidation from Infrared Spectra |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2508.16112 |