Keywords: Materials Discovery, Synthetic Data, Evaluation Metrics, Scoring Metrics for Generative Models, GANs
TL;DR: In this paper, we explore new and existing methods of evaluating synthetic material micro-structure images from generative models.
Abstract: 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 Frechet 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.
Paper Track: Proposals
Submission Category: AI-Guided Design, Automated Material Characterization