Intelligent Mining of Infrared Spectral Data based on Multimodal Information Extraction of Catalytic Reaction in Scientific Lite

Accurate interpretation of infrared spectral features of the molecular intermediate states is essential for understanding the mechanisms in catalytic reactions. Spectral convolution caused by overlapping vibrational signatures from reaction intermediates poses critical challenges for conventional data analysis, creating an urgent demand for intelligent analytical solutions to accelerate analysis processing. However, building a sufficient experimental dataset for training deep learning models is a critical issue because experiments are very time-consuming. Here, we propose a multimodal information extraction framework that integrates and modifies target detection, character recognition, constructed color recognition, named entity recognition and data processing algorithms, to systematically retrieve key experimental data from the infrared spectroscopy plots and textual descriptions of extensive catalysis scientific literature. This work establishes a structured foundation for constructing an infrared spectroscopy knowledge base, which supports general deep learning model training of \\textit{in-situ} infrared spectral interpretation.