Modeling and Predicting the Glass Transition Temperature of Polymethacrylates Based on Quantum Chemical Descriptors by Using Hybrid PSO-SVR

2012 ◽  
Vol 22 (1) ◽  
pp. 52-60 ◽  
Author(s):  
Jun-Fang Pei ◽  
Cong-Zhong Cai ◽  
Yi-Ming Zhu ◽  
Bin Yan
Keyword(s):  
Glass Transition ◽  
Transition Temperature ◽  
Glass Transition Temperature ◽  
Quantum Chemical ◽  
Quantum Chemical Descriptors ◽  
Chemical Descriptors

Machine learning glass transition temperature of polyacrylamides using quantum chemical descriptors

2021 ◽  
Vol 12 (6) ◽  
pp. 843-851 ◽  
Author(s):  
Yun Zhang ◽  
Xiaojie Xu
Keyword(s):  
Machine Learning ◽  
Glass Transition ◽  
Transition Temperature ◽  
Glass Transition Temperature ◽  
Quantum Chemical ◽  
Quantum Chemical Descriptors ◽  
Model Based ◽  
Chemical Descriptors ◽  
High Degree

Polyacrylamides glass transition temperature predictions from different models, where the GPR model is from the current study. The GPR model based on quantum chemical descriptors shows a high degree of accuracy.

Prediction of Glass Transition Temperature of Polymer by Support Vector Regression

2012 ◽  
Vol 455-456 ◽  
pp. 436-442
Author(s):  
J.F. Pei ◽  
C.Z. Cai ◽  
X.J. Zhu ◽  
G.L. Wang ◽  
B. Yan
Keyword(s):  
Glass Transition ◽  
Transition Temperature ◽  
Glass Transition Temperature ◽  
Support Vector Regression ◽  
Density Functional ◽  
Absolute Error ◽  
Percentage Error ◽  
Support Vector ◽  
Quantum Chemical Descriptors ◽  
Leave One Out

. Based on two quantum chemical descriptors (the thermal energy Ethermal and the total energy of the whole system EHF) calculated from the structures of the repeat units of polyacrylamides by 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 (Tg) of polyacrylamides. The prediction performance of SVR was compared with that of multivariate linear regression (MLR). The results show that the mean absolute error (MAE=4.65K), mean absolute percentage error (MAPE=1.28%) and correlation coefficient (R2=0.9818) calculated by leave-one–out cross validation (LOOCV) via SVR models are superior to those achieved by QSPR (MAE=14.25K, MAPE=4.39% and R2=0.9211) and QSPR-LOO (MAE=17.01K, MAPE=5.66% and R2=0.8823) models for the identical samples, respectively. The prediction results strongly demonstrate that the modeling and generalization abilities of SVR model consistently surpass those of QSPR and QSPR-LOO models. It is revealed that the established SVR model is more suitable to be used for prediction of the Tg values for unknown polymers possessing similar structure than the conventional MLR approach. These suggest that SVR is a promising and practical methodology to predict the glass transition temperature of polyacrylamides.

Modeling the Glass Transition Temperature of Polymers via Multipole Moments Using Support Vector Regression

2012 ◽  
Vol 455-456 ◽  
pp. 430-435 ◽  
Author(s):  
J.F. Pei ◽  
C.Z. Cai ◽  
X.J. Zhu ◽  
G.L. Wang ◽  
B. Yan
Keyword(s):  
Glass Transition ◽  
Transition Temperature ◽  
Glass Transition Temperature ◽  
Support Vector Regression ◽  
Percentage Error ◽  
Support Vector ◽  
Structure Property ◽  
Multipole Moments ◽  
Quantum Chemical Descriptors ◽  
Qspr Model

. This study introduces support vector regression (SVR) approach to model the relationship between the glass transition temperature (Tg) and multipole moments for polymers. SVR was trained and tested via 60 samples by using two quantum chemical descriptors including the molecular traceless quadrupole moment and the molecular average hexadecapole moment Φ. The prediction performance of SVR was compared with that of reported quantitative structure property relationship (QSPR) model. The results show that the mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) of training samples and test samples achieved by SVR model, are smaller than those achieved by the QSPR model, respectively. This investigation reveals that SVR-based modeling is a practically useful tool in prediction of the glass transition temperature of polymers.

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