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

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...