Infrared Spectra Prediction for Diazo Groups Utilizing a Machine
  Learning Approach with Structural Attention Mechanism
Chengchun Liu Fanyang Mo
Infrared (IR) spectroscopy is a pivotal technique in chemical research for
elucidating molecular structures and dynamics through vibrational and
rotational transitions. However, the intricate molecular fingerprints
characterized by unique vibrational and rotational patterns present substantial
analytical challenges. Here, we present a machine learning approach employing a
Structural Attention Mechanism tailored to enhance the prediction and
interpretation of infrared spectra, particularly for diazo compounds. Our model
distinguishes itself by honing in on chemical information proximal to
functional groups, thereby significantly bolstering the accuracy, robustness,
and interpretability of spectral predictions. This method not only demystifies
the correlations between infrared spectral features and molecular structures
but also offers a scalable and efficient paradigm for dissecting complex
molecular interactions.