Computational materials design integrates targeted materials process-structure and structure-property models in systems frameworks to meet specific engineering needs. The microstructure representations have to satisfy certain statistical parameters to be considered acceptable for further design processes. So, representation of microstructures have to be accurately identified to be considered for materials design. Current techniques have certain limitations in the characterization and reconstruction of these microstructures. The current state-of-the art model-based approaches do not have sufficient parameters that can serve as design variables. The high dimensional nature of this problem relies on dimension reduction that tends to lose important microstructural information. So, in the proposed project we want to design a methodology based on deep adversarial networks to produce these microstructures. The whole framework will be based on generative adversarial networks (GAN) and use them to learn the mapping between latent variables and microstructures. The idea is to train the GAN network to obtain microstructures that are statistically accurate and satisfy certain predefined properties.