Advancing computational spectroscopy: machine learning approaches for reconstructing incomplete spectroscopic and collisional datasets

Spectroscopy remains a cornerstone of modern physics and chemistry, providing critical insights into molecular structures and interstellar phenomena.Accurate interpretation of interstellar line spectra through radiative transfer modeling relies heavily on two essential types of molecular input data: spectroscopic information (including energy levels, transition probabilities, and statistical weights) and collisional data.However, the completeness and precision of these datasets are often limited, constraining the reliability of astrophysical models.This work explores the application of machine learning (ML) and artificial neural networks (ANNs) for predicting and reconstructing missing spectroscopic/collisional data.We analyze their potential to enhance spectral databases, thereby improving stellar spectral analyses and the determination of fundamental stellar parameters.The study also addresses key challenges, methodological limitations, and validation issues inherent to data-driven approaches.Finally, we discuss current progress, share practical experiences, and outline future prospects and research needs in the rapidly evolving field of computational spectroscopy.