  As data-driven methods rise in popularity in materials science applications,
a key question is how these machine learning models can be used to understand
microstructure. Given the importance of process-structure-property relations
throughout materials science, it seems logical that models that can leverage
microstructural data would be more capable of predicting property information.
While there have been some recent attempts to use convolutional neural networks
to understand microstructural images, these early studies have focused only on
which featurizations yield the highest machine learning model accuracy for a
single data set. This paper explores the use of convolutional neural networks
for classifying microstructure with a more holistic set of objectives in mind:
generalization between data sets, number of features required, and
interpretability.
