APPLICATION OF NEURAL-NETWORK APPROACH FOR PREDICTION OF SOME THERMOCHEMICAL PROPERTIES OF ALKANES

A standard feed-forward neural network with one layer of hidden neurons for prediction of boiling points, melting points, critical temperatures, and molar volumes of all alkanes with up to ten carbon atoms has been used. The descriptors (input activities) of alkanes are determined as the embedding frequencies of eight preselected alkane-like substructures. These nonnegative integer entities determine numbers of appearance of the given substructure in an alkane. The obtained results are very encouraging for wider applications of neural networks for prediction, classification and correlation of molecular properties based on their substructure fragments.