Prediction of the glass transition temperatures of styrenic copolymers using a QSPR based on the DFT method

A quantitative structure-property relationship (QSPR) model, based on three quantum chemical descriptor (α, q+ and Cv) obtained from the monomers using the density functional theory (DFT) method, is developed to predict the glass transition temperature (Tg) of random copolymers, such as poly(styrene-co-acrylamide) (SAAM), poly(styrene-co-acrylic acid) (SAA), poly(styrene-co-acrylonitrile) (SAN), poly(styrene-co-butyl acrylate) (SBA), poly(styrene-co-methyl acrylate) (SMA), poly(styrene-co-ethyl acrylate) (SEA) and poly(acrylonitrile-co-methyl acrylate) (ANMA). The QSPR models having correlation coefficient R of 0.991 and the standard error s of 6.924 K gives a good enough predictive power with the average error (AE) of 1.606%. This paper provides a new insight for the QSPR study of glass transition temperatures of random copolymers.