  Identifying the key microstructure representations is crucial for
Computational Materials Design (CMD). However, existing microstructure
characterization and reconstruction (MCR) techniques have limitations to be
applied for materials design. Model-based MCR approaches do not have parameters
that can serve as design variables, while MCR techniques that rely on dimension
reduction tend to lose important microstructural information. In this work, we
present a deep adversarial learning methodology that overcomes the limitations
of existing MCR techniques. In the proposed methodology, generative adversarial
networks (GAN) are trained to learn the mapping between latent variables and
microstructures. Thereafter, the low-dimensional latent variables serve as
design variables, and a Bayesian optimization framework is applied to obtain
microstructures with desired material property. Due to the special design of
the network architecture, the proposed methodology is able to identify the
latent (design) variables with desired dimensionality, as well as minimize the
information loss even for complex material microstructures. The validity of the
proposed methodology is tested numerically on a synthetic microstructure
dataset and its effectiveness for materials design is evaluated through a case
study of optimizing optical performance for energy absorption. In addition, the
scalability and transferability of proposed methodology are also demonstrated
in this work. Specifically, the proposed methodology is scalable to generate
arbitrary sized microstructures, and it can serve as a pre-trained model to
improve the performance of a structure-property predictive model via transfer
learning.
