Use of a neural network to determine the boiling point of alkanes

Abstract

Back-propagation neural networks (NNs) are useful for the study of quantitative structure–activity relationships or structure–property correlations. Models of relationships between structure and boiling point (bp) of 150 alkanes were constructed by means of a multilayer neural network (NN) using the back-propagation algorithm. The results of our NN were compared with those of other models from the literature, and found to be better. The boiling points of the 150 alkanes were then predicted by removing 15 compounds (test set) and using the 135 other molecules as a training set. Using the same process, all the compounds in the data bank were then predicted in groups of 15 compounds. The results obtained were satisfying.