where y are linearly interpolated descriptors of real and fake environments of atoms of the same type. This step is applied ten times per generator update, to ensure a smooth surface of the critic predictions. The significant advantage of the WGAN compared to the original GAN is its more stable training due to the WGAN's reliance on the Wasserstein-distance as opposed to the GAN's Kullback-Liebler divergence. For a WGAN to have stable training the critic model has to obey the Lipschitz contraint C(P i ) -C(P j ) < K P i -P j where P i , P j are two local descriptors, K is an arbitrary positive constant and C(P i ) corresponds to the prediction of the critic on P i .