IRC+10216 is a carbon-rich asymptotic giant branch star surrounded by a dense, chemically rich circumstellar envelope (CSE). Although a diverse array of molecules has been detected, the full molecular inventory of the envelope and the formation pathways of more complex species remain poorly understood. This study aims to systematically identify plausible new molecular candidates in the CSE of IRC+10216, predict their column densities, and prioritize the most promising targets for observational detection using a combined cheminformatics and machine learning approach. We conducted a structural similarity search based on known molecular species using extended connectivity fingerprints, retrieving 1,133 plausible candidates from chemical databases. A support vector regression model was trained to predict their column densities. The resulting candidates were filtered using criteria based on elemental abundance, kinetic plausibility, and spectral line intensities to identify observationally feasible targets. The filtering process reduced the candidate list to 30 high-priority molecules with entries in spectroscopic catalogs. Density functional theory calculations provided key molecular properties for these species, including optimized geometries, formation energies, dipole moments, zero-point vibrational energies, and rotational constants. The integrated framework developed here enables efficient identification and prioritization of plausible molecular candidates in IRC+10216.
Li et al. (Mon,) studied this question.