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Modeling and Predicting the Glass Transition Temperature of Polymethacrylates Based on Quantum Chemical Descriptors by Using Hybrid PSO‐SVR
Author(s) -
Pei JunFang,
Cai CongZhong,
Zhu YiMing,
Yan Bin
Publication year - 2013
Publication title -
macromolecular theory and simulations
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.37
H-Index - 56
eISSN - 1521-3919
pISSN - 1022-1344
DOI - 10.1002/mats.201200072
Subject(s) - quantum chemical , glass transition , set (abstract data type) , training set , test set , mean squared error , mathematics , computer science , artificial intelligence , biological system , chemistry , statistics , molecule , organic chemistry , polymer , biology , programming language
Based on six quantum chemical descriptors (| L ‐1.356|, E total , q C6, α , q − , and E therm ), the hybrid PSO‐SVR is proposed to establish a model for predicting the glass transition temperature ( T g ) of 37 polymethacrylates. The prediction performance of SVR was compared with those of reported MLR and ANN models. The results show that the RMSE , MAPE , and R 2 calculated by SVR are superior to those achieved by MLR or ANN model for the identical training set and test set. This investigation reveals that the SVR model is more suitable to be used for prediction of the T g values for unknown polymethacrylates possessing similar structure than the conventional MLR or ANN model, and provides a clue that the method proposed in this study may be useful in computer‐aided design of new polymethacrylates with desired T g .