Generative Adversarial Networks‐Based Synthetic Microstructures for Data‐Driven Materials Design

To understand the material paradigm, data‐driven material design necessitates both microstructural input and output in the form of visual images. Therefore, generative adversarial networks (GAN)‐based deep convolutional GAN, cycle‐consistent GAN, and super‐resolution GAN techniques are used to generate, translate, and improve the quality of microstructural images in this study. The reconstructed virtual microstructural images are realistic and indistinguishable from the real ones. Furthermore, using GAN techniques to reconstruct microstructural image suggests promising ways to design desired microstructures using parameterized descriptors and image augmentation, which are expected to advance data‐driven materials research. Data‐driven material design necessitates both microstructural input and output in the form of visual images. In this study, generative adversarial networks (GAN)‐based deep convolutional GAN, cycle‐consistent GAN, and super‐resolution GAN techniques are applied to generate, translate, and improve the quality of microstructural images. The reconstructed virtual microstructural images are realistic and indistinguishable from the real ones.