In this study, a new neural network quantitative structure-property relationship model for prediction of θ (LCST ) of polymer solutions is presented. The parameters of this model are eight molecular descriptors which are calculated only from the chemical structure of polymer and solvent. These eight molecular descriptors were selected from 3328 molecular descriptors of polymer and solvent available in polymer solution by genetic algorithm-based multivariate linear regression (GA-MLR) technique. The obtained neural network model can predict the θ (LCST ) of 169 polymer solutions with mean relative error of 1.67% and squared correlation coefficient of 0.9736.
© 2013 by Walter de Gruyter GmbH & Co.