In silico prediction of free-radical chain transfer constants for some organic agents in styrene polymerization

Abstract

In the present work, quantitative structure–reactivity relationship (QSRR) approaches were used to predict the chain transfer constant log C _x of some organic compounds as chain transfer agents in free-radical polymerization of styrene. The energy of the lowest unoccupied molecular orbital, hydrogen-bonding-dependent hydrogen donor charged area, first-order Kier and Hall index, final heat of formation/number of atoms, count of H donor sites, and Min>(0.1) bond order of a C atom were selected as the most relevant variables from the pool of calculated descriptors by the stepwise multiple regression feature selection method. Then, an artificial neural network and multiple linear regressions were utilized to construct the nonlinear and linear QSRR models. The standard errors in the prediction of log C _x by the linear regression model were 0.641, 0.964, and 0.843 and by the neural network model were 0.049, 0.076, and 0.090 for training, internal, and external test sets, respectively. The predictivity of the artificial neural network model was further examined by cross-validation methods, which produce a Q ² value of 0.85. The results of this study revealed the applicability of QSRR approaches in prediction of the chain transfer constant by using an artificial neural network.

Graphical abstract