GANs are generative models which consist of two components, a generator and a discriminator, mainly implemented by neural networks. 'Adversarial' in GANs signifies the competitive interaction between the generator and discriminator, where the former creates new data instances that resemble the true data, i.e., the training data and shares them with the latter, then the discriminator evaluates the generated instances and distinguishes them from real data as accurately as possible. Both networks evolve together: the generator tries to produce more realistic data to fool the discriminator, while the discriminator strives to become better at identifying fake data received from the generator. The training dynamics is a min-max optimization problem, where the generator minimizes the likelihood of the discriminator correctly classifying its outputs as fake, and simultaneously, the discriminator maximizes its accuracy in identifying real from fake. Over time, this adversarial process leads to Nash equilibrium 109 where the generator creates highly realistic data, meanwhile, the discriminator becomes highly adept at discriminating subtle differences between true and generated data. This dynamic optimization is shown in Figure . To design complex-architecture materials without prior knowledge, Mao et al. used GANs to analyze millions of various crystallographic architectures sampled from simulation. Both configurations and their properties were used to map the design space so that the discriminator interpolates the implicit relationships between configurations and properties, which guides the generators to promptly generate new configurations with expected properties. For example, the proposed GAN model designed more 400 2D architectures with the HS upper bounds of stiffness at various porosities.