We presented LoGAN, a proof-of-concept implementation of a novel Wasserstein-GAN based architecture for atomic structure prediction of various systems. We combine ideas from previous GAN implementations with approaches seen in neural-network force fields to optimize our model for the efficient use of training data. We also introduce a selection step in our pipeline to find the unique structures predicted by our models, thus avoiding wasting large amount of computational power to evaluate the properties of similar candidates.