DFT-based theoretical QSPR models of Q-e parameters for the prediction of reactivity in free-radical...

Density functional theory (DFT) calculations at the B3LYP/6-31G(d) level were carried out for 47 vinyl monomers with structures C(1)H2 = C(2)HR3, and the calculated quantum chemical descriptors were used to construct quantitative structure-property relationship (QSPR) models of the reactivity parameters of monomers Q and e. Stepwise multiple linear regression analysis (MLRA) and artificial neural networks (ANN) were adopted to generate the models. Simulated with the final optimum back-propagation (BP) neural networks, the results show that predicted lnQ and e values are in good agreement with experimental data, with test sets possessing correlation coefficients of 0.982 for lnQ and 0.943 for e. The proposed ANN models have better prediction ability than existing models.