We trained 20 LoGAN models for each selected target structure (5 models per hyperparameter setting), with R cut = 4 Å and N max = 4 while using all four combinations of Gaussian or Bessel radial functions with either 12 or 25 maximum neighbour atoms. For each combination, we generated 5,000 structures using the five models (1,000 structures per individual model), leading to 20,000 structures overall. We then applied the previously described filtering algorithm to identify 20 structures for relaxation for each of the four hyperparameter sets. Fig. depicts the clustered structures obtained from a PCA of the perstructure Bessel descriptors of all 20,000 generated structures. The spread of the points and the clear clusters show that the 2D projection of the structural descriptors is able to distinguish between different structures, and that the k-means clustering algorithm is able to pick unique samples for relaxation. The insets in Fig. furthermore depict a few structures (prior to DFT relaxation). Each structure is mainly made up of Si-O tetrahedra, which are then arranged differently inside the unit cell. This showcases again that the local structure is similar (silicon prefers to be surrounded by four oxygen atoms in a tetrahedral arrangement), while the global structure differs vastly, making the LoGAN approach both viable and efficient.