PREDICTION OF NORMAL BOILING POINTS FOR A DIVERSE SET OF INDUSTRIALLY IMPORTANT ORGANIC-COMPOUNDS FROM MOLECULAR-STRUCTURE

Models that accurately predict normal boiling points for organic compounds containing heteroatoms have been developed with regression and computational neural network methods. The structures of the compounds are represented by calculated structural descriptors. Two models are presented-one for a set of 277 compounds containing only O, S, and halogens, and a second for a set of 104 compounds all containing N. Root-mean-square errors of about 9 K result. The accuracy of prediction of these models is compared to a widely used group contribution method for boiling point estimation.