GANs have been extensively applied to facilitate the exploration of inorganic material space, but they also have some shortcomings. First, difficulties in convergence, the adversarial loop between the generator and the discriminator makes the model difficult to train and converge. Second, mode collapse, previous studies observed that the generator sometimes repeatedly generates new data with a certain type of modes, although a wider variety of modes are included in the data distribution. To tackle this issue, various variants of GANs have been proposed. The first solution is to avoid a fake local Nash equilibrium. Studies show that model collapse happens along with sharp gradients of the discriminator. To avoid this, DRAGAN proposed to regularize the discriminator by adding extra penalization terms so that its gradients are realistically constrained. Similarly, LSGANs 118 used least squares loss to replace the original cross-entropy loss in the discriminator to overcome the vanishing gradient issue. Finally, several controversial topics, such as the generalization ability 119 and memorization issues, 120 are still being discussed with respect to GANs.