Prediction of the Glass Transition Temperatures of Styrenic Copolymers by Using Support Vector Regression Combined with Particle Swarm Optimization

摘要

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 (C_v ) 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 quantitative structure-property relationship (QSPR)/multivariate linear regression (MLR) model for the identical training set, whereas the MAE = 3.03 K, MAPE = 0.90%, and R 2 = 0.9952 calculated by SVR also outperform those (MAE = 5.38 K, MAPE = 1.61%, and R 2 = 0.9778) achieved by the QSPR/MLR model for the identical validation set, respectively. The prediction results strongly support that the modeling and generalization ability of the SVR model consistently surpasses that of the QSPR/MLR model by applying identical training and validation samples. It is demonstrated that the established SVR model is more suitable to be used for prediction of the T _g values for unknown polymers possessing similar structure than the conventional MLR model. It is also shown that the hybrid PSO-SVR approach is a promising and practical methodology to predict the glass transition temperature of styrenic copolymers.View full textDownload full textKeywordscopolymer, glass transition temperature, PSO, regression analysis, SVRRelated var addthis_config = { ui_cobrand: "Taylor & Francis Online", services_compact: "citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,more", pubid: "ra-4dff56cd6bb1830b" }; Add to shortlist Link Permalink http://dx.doi.org/10.1080/00222348.2011.629908