Interpretable Convolutional Networks -Applications for Materials

Abstract

In this study, we constructed two different neural network models. One is a convolutional neural network model for evaluation of the quality of catalysts from a set of nanoscale images. Another is a graph convolutional neural network model for evaluation of the predict glass transition temperature (Tg) of polyesters. We further applied the gradient-based method to visualize saliency maps to understand which nanostructures or chemical structures will affect the performance. Along this line, approaches based on integrated gradients will be significantly more effective for structural characterization tasks and may save both time and costs required for the design and development of materials.