Inferring low-dimensional microstructure representations using   convolutional neural networks
  We apply recent advances in machine learning and computer vision to a central problem in materials informatics: The statistical representation of microstructural images. We use activations in a pre-trained convolutional neural network to provide a high-dimensional characterization of a set of synthetic microstructural images. Next, we use manifold learning to obtain a low-dimensional embedding of this statistical characterization. We show that the low-dimensional embedding extracts the parameters used to generate the images. According to a variety of metrics, the convolutional neural network method yields dramatically better embeddings than the analogous method derived from two-point correlations alone. 
Nicholas Lubbers, Turab Lookman, Kipton Barros
physics.comp-ph cond-mat.mtrl-sci cs.CV