For each hyperparameter setting, each of the 20 structures identified by clustering was then geometry-optimized using DFT. Fig. depicts the energy distribution of the relaxed structures for each target. As a baseline, Fig. furthermore depicts DFT relaxations started from 80 randomly generated structures, where we enforced that atoms cannot be placed closer than 0.9d min , where d min is the smallest distance between atoms in the goal structure. For each of the six target structures, the developed WGAN outperforms random initialization by a large margin. Namely, the WGAN yields lower energy structures (Fig. ) and required less relaxation steps to reach convergence (not shown). Most importantly, the union of all models was able to find 100% of the target structures, while the random search only yielded a success rate of 50%. With the LoGAN approach, we are therefore able to also identify new crystal structures based on only a small set of training data without any expert knowledge in a fast and data-efficient fashion.