Infrared Spectra\nPrediction for Functional Group Region\nUtilizing a Machine Learning Approach with Structural Neighboring\nMechanism

Infrared (IR) spectroscopy is a pivotal technique in\nchemical research\nfor elucidating molecular structures and dynamics through vibrational\nand rotational transitions. However, the intricate molecular fingerprints\ncharacterized by unique vibrational and rotational patterns present\nsubstantial analytical challenges. Here, we present a machine learning\napproach employing a structural neighboring mechanism tailored to\nenhance the prediction and interpretation of infrared spectra. Our\nmodel distinguishes itself by honing in on chemical information proximal\nto functional groups, thereby significantly bolstering the accuracy,\nrobustness, and interpretability of spectral predictions. This method\nnot only demystifies the correlations between infrared spectral features\nand molecular structures but also offers a scalable and efficient\nparadigm for dissecting complex molecular interactions.