Spectroscopy learning: A machine learning method for study diatomic vibrational spectra including dissociation behavior

Molecular spectroscopy plays an important role in the study of physical and chemical phenomena at the atomic level. However, it is difficult to acquire accurate vibrational spectra directly in theory and experiment, especially these vibrational levels near the dissociation energy. In our previous study (Variational Algebraic Method), dissociation energy and low energy level data are employed to predict the ro-vibrational spectra of some diatomic system. In this work, we did the following: 1) We expand the method to a more rigorous combined model-driven and data-driven machine learning approach (Spectroscopy Learning Method). 2) Extracting information from a wide range of existing data can be used in this work, such as heat capacity. 3) Reliable vibrational spectra and dissociation energy can be predicted by using heat capacity and the reliability of this method is verified by the ground states of CO and Br(2) system.