In most WGANs the output of the generator model is directly fed into the critic, thus the linearly interpolated points where the gradient penalty is enforced lie in the actually accessible regions of the intermediate representation space. In our case this is not true, since the generator outputs are post-processed and turned into local descriptors before criticism, and the interpolated descriptors might not relate to geometries accessible to the generator. This potentially hinders the training of our generator, since the loss surface experienced by it might still be ragged. As a partial resolution we tested replacing the spherical Bessel radial function, g in Eq. ( ), by Gaussian radial basis functions similar to those used in the original SOAP descriptors. While these descriptors do not satisfy the same conditions for optimal completeness as their spherical Bessel function-based counterparts, they depend monotonically on |R|. This means that if we compare the descriptors of three structures where the distances measured from the central atom are scaled with c 1 , c 2 and c 3 , with c 1 < c 2 < c 3 and the directions are unchanged, then all descriptor values of the c 2 scaled structure will lie between the descriptors of the c 1 and c 3 scaled structures. While we wanted to note this theoretical drawback of our model, we did not find significant differences between the two kind of descriptors in our tests. In the following we use both, since larger variety in the trained models can lead to larger variety in predicted structures.