Localizing and quantifying the intra-monomer contributions to the glass transition temperature using artificial neural networks

Abstract

We used fully connected artificial neural networks (ANN) to localize and quantify, based on the monomer structure of several polymers, the specific features responsible for their observed glass transition temperatures (Tg). The use of ANNs allows us not only to successfully predict the Tg of the polymers but, even more important, to understand what parts of the monomer are mainly contributing to it. For this task, we used the weights of a trained ANN as obtained after fitting the input data (monomer structure) to the corresponding Tg value. The study was performed for a set of more than 200 atactic acrylates for which typical Tg defining features were identified. Thus, the ANN is able to recognize the relevance of the backbone stiffness, the length of pending groups or the presence of methyl groups on the value of the glass transition temperature. This approach can be easily extended to many other interesting properties of polymers and it is worth noting that only the monomer chemical structure is needed as input. This method is potentially useful for identifying orthogonal ways of tuning polymer properties during the design and development of new materials and it is expected that it will contribute to a better understanding of the polymer's behavior.