A NEURAL NETWORK STUDY ON GLASS TRANSITION TEMPERATURE OF POLYMERS

摘要

In this paper, an artificial neural network model is adopted to study the glass transition temperature of polymers. In our artificial neural networks, the input nodes are the characteristic ratio C∞, the average molecular weight M, between entanglement points and the molecular weight Mmon of repeating unit. The output node is the glass transition temperature Tg,and the number of the hidden layer is 6. We found that the artificial neural network simulations are accurate in predicting the outcome for polymers for which it is not trained. The maximum relative error for predicting of the glass transition temperature is 3.47%, and the overall average error is only 2.27%. Artificial neural networks may provide some new ideas to investigate other properties of the polymers.