  The evaluation of synthetic micro-structure images is an emerging problem as
machine learning and materials science research have evolved together. Typical
state of the art methods in evaluating synthetic images from generative models
have relied on the Fr\'echet Inception Distance. However, this and other
similar methods, are limited in the materials domain due to both the unique
features that characterize physically accurate micro-structures and limited
dataset sizes. In this study we evaluate a variety of methods on scanning
electron microscope (SEM) images of graphene-reinforced polyurethane foams. The
primary objective of this paper is to report our findings with regards to the
shortcomings of existing methods so as to encourage the machine learning
community to consider enhancements in metrics for assessing quality of
synthetic images in the material science domain.
