A Machine Learning and Quantum Chemistry Approach for Identifying Prebiotic Molecules in the Interstellar Medium

The discovery of molecules in the interstellar medium (ISM) plays a key role in understanding prebiotic chemistry. Relatively few (~250) molecules have been confirmed in the ISM, and detecting additional species is crucial for expanding our knowledge of astrochemical processes. We present a strategy for predicting possible prebiotic molecules in the ISM that combines machine learning and high-accuracy quantum chemistry calculations. Using a reaction dataset of over 153,000 possible combinations of known interstellar molecules, we applied a machine learning model to estimate reaction energy barriers and identify those with low or zero barriers that are most likely to occur in the ISM. From this screening process, 24 molecules were identified, five of which have already been observed in interstellar space. For the remaining 19 molecules, we conducted density functional theory (DFT) and coupled cluster theory calculations to determine the most stable conformers, spectroscopic parameters, and predict their detectability through spectroscopy. We present data to guide future observational searches for new interstellar species, contributing to the ongoing exploration of complex organic molecules in space and their potential role in prebiotic chemistry.