We have thus presented, for the first time, an efficient and versatile generative approach for the low-data and variable stoichiometry regime, which can generate both periodic and non-periodic structures. Due to its ease of use, fast training and inference, and clever filtration of the generated structures, we envision LoGAN to be applicable to diverse fields of research relying on the generation of plausible candidate structures.