Prediction of the Glass Transition Temperatures of Styrenic Copolymers by Using Support Vector Regression Combined with Particle Swarm Optimization
Based on three quantum chemical descriptors (the average polarizability of a molecule (α), the most positive net atomic charge on hydrogen atoms in a molecule (q+ ) and the heat capacity at constant volume (Cv ) derived from the monomers using the density functional theory (DFT), the support vector regression (SVR) approach combined with particle swarm optimization (PSO), is proposed to establish a model for prediction of the glass transition temperature (T g) of random copolymers including 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 mean absolute error (MAE = 1.6 K), mean absolute percentage error (MAPE = 0.45%), and correlation coefficient (R 2 = 0.9978) calculated by SVR model are superior to those (MAE = 5.47 K, MAPE = 1.51%, and R 2 = 0.9829) achieved by a quantita...