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Prediction of Glass Transition Temperature Using Hybrid Neural Networks

Shivani Syal, James M. Caruthers, and Venkat Venkatasubramanian. Chemical Engineering, Purdue University, 480 Stadium Mall Drive, West Lafayette, IN 47907

The prediction of polymer properties from their monomers or repeat unit structures is a challenging problem in polymer science and technology. Predicting the relationship between structure and property is difficult when trying to predict large changes in properties that occur from significant changes in chemical composition for different classes of polymers. However, the challenge increases when one tries to predict more subtle changes that occur with smaller chemical variations within a specific class of polymers. For example, polycarbonates are used in a number of high performance applications, where small improvements in properties have important engineering consequences. An important property that dictates the processing and ultimate end use application of such polymers is the glass transition temperature. In this paper we report on the development of a correlation to describe how the glass transition temperature depends on the molecular structure.

The complexity of this problem dissuades the use of a purely first-principles approach. Consequently, a hybrid approach based on quantitative structure-property relationships will be employed, using fundamental knowledge in tandem with artificial intelligence tools like computational neural networks (CNN). We will present results for the performance of the CNN for a number of bisphenol A-polycarbonate derivatives using a variety of descriptors. Topological descriptors that abstract information at the level of molecular structure have been studied using several neural network architectures such as the radial-basis function (RBF) and multilayer feed-forward. A bootstrap cross-validation procedure was employed, which removes the bias in the selection of training and test sets, minimizes the possibility of chance correlations and provides more robust generalization capabilities. About 200 learners were separately trained-and-tested using bootstrap making this approach more rigorous than a single random train-and-test partition. The estimated error for the aggregate learner is taken as the average of the error over the individual learners. Sensitivities of the networks were explored with respect to changes in the activation function, number of hidden layer neurons and the initial weight vector. For the RBF network, the glass transition temperatures of 64% of the polymers were predicted to within 10C of their actual values (i.e. R2=0.9275 and RMS=11.6C) while the number improved to 73% with the feedforward ensemble (i.e. R2=0.9548 and RMS=9.4C). The results are promising despite the use of topological indices that have only a limited physical basis as descriptors for Tg. We will discuss the opportunities of using more physically relevant descriptors like flexibility, configurational entropy and packing in conjunction with CNNs for predicting Tg.