IR-Agent: Expert-Inspired LLM Agents for Structure Elucidation from Infrared Spectra

2025-08-22

AI Summary

Existing infrared (IR) spectral interpretation methods struggle to replicate expert analytical reasoning and lack flexibility in integrating heterogeneous chemical knowledge. To address this, we propose a large language model (LLM)-based multi-agent reasoning framework that decomposes expert spectral analysis into collaborative, specialized agents—each dedicated to distinct tasks such as spectral preprocessing, functional group identification, and structural constraint reasoning. Leveraging a chemistry-knowledge injection mechanism and a modular, dynamic coordination strategy, the framework enables interpretable, knowledge-driven inference. Evaluated on a real-world IR spectral dataset, our approach significantly outperforms mainstream baselines, achieving a 12.7% average accuracy gain. Moreover, it supports adaptive integration of diverse chemical information—including molecular formulas, reaction rules, and literature-derived empirical knowledge—thereby establishing a scalable, verifiable paradigm for intelligent spectral interpretation.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Knowledge Representation Languages
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Emulating expert-driven infrared spectral analysis procedures
• Incorporating diverse chemical knowledge flexibly into analysis
• Improving accuracy of molecular structure elucidation from spectra

Innovation

• Multi-agent framework emulating expert analysis
• Specialized agents for integrated IR interpretation
• Extensible design adaptable to chemical information