Prediction of Refractive Index of Polymers Using Artificial Neural Networks

Density functional theory (DFT) calculations were carried out in the prediction of the refractive index (n) of different polymers at the B3LYP/6-31G(d) level. A set of quantum chemical descriptors calculated from monomers of polymers, the energy of the lowest unoccupied molecular orbital (E-LUMO), molecular average polarizabihty (alpha), heat capacity at constant volume (C-V), and the most positive net atomic charge on hydrogen atoms in a molecule (e) were used to build a general quantitative structure-property relationship (QSPR) model for the refractive index. The proposed model gives the mean error of prediction of 1.048 % for the validation set.