QSPR analysis for intrinsic viscosity of polymer solutions by means of GA-MLR and RBFNN

Abstract

A quantitative structure–property relationship (QSPR) treatment of intrinsic viscosity of polymer solutions was performed by means of a genetic algorithm based multivariate linear regression (GA-MLR). A five parameters correlation, with squared correlation coefficient R2=0.8275 gives good predictions for 65 polymer solutions. In preparation of this model, 1664 molecular descriptors for each polymer and 1664 molecular descriptors for each solvent were checked and finally, five molecular descriptors were selected. For considering the nonlinear behavior of these five molecular descriptors, a radial based function neural network (RBFNN) with squared correlation coefficient R2=0.9100 was constructed. Notably, all the parameters involved in these equations can be derived solely from the chemical structure of the polymers repeating unit and the solvents which makes them very useful for prediction of the intrinsic viscosity of unknown or unavailable polymer solutions.