Artificial neural network prediction of glass transition temperature of polymers

作者：Liu, Wanqiang^*; Cao, Chenzhong

来源：Colloid and Polymer Science, 2009, 287(7): 811-818.

DOI：10.1007/s00396-009-2035-y

• Artificial neural network
• Density function theory
• Glass transition temperature
• QSPR

In this article, the molecular average polarizability alpha, the energy of the highest occupied molecular orbital E (HOMO), the total thermal energy E (thermal), and the total entropy S were used to correlate with glass transition temperature T (g) for 113 polymers. The quantum chemical descriptors obtained directly from polymer monomers can represent the essential factors that are governing the nature of glass transition in polymers. Stepwise multiple linear regression (MLR) analysis and back-propagation artificial neural network (ANN) were used to generate the model. The final optimum neural network with 4-[4-2](2)-1 structure produced a training set root mean square error (RMSE) of 11 K (R = 0.973) and a prediction set RMSE of 17 K (R = 0.955). The results show that the ANN model obtained in this paper is accurate in the prediction of T (g) values for polymers.

• 出版日期2009-7
• 单位湖南科技大学