Table shows the mean absolute errors of the formation energy models for each representation and their comparison with CGCNN. Rep1 has comparably high MAE in comparison to other representations, which is understandable as it doesn't contain any information about the elemental properties. Notably, the two representations (Rep2 and Rep3) consisting of elemental properties have comparable errors with respect to CGCNN, giving us high confidence in their modeling capabilities. Rep3 performs marginally better than Rep2, but it has a significantly higher number of properties. Discretization of the large number of columns for the generation model would increase the dimensionality of the problem, effectively increasing the computational complexity and simultaneously making it harder for the VAE to correctly generate the samples. Rep2 is onehot encoded, which makes it more suitable for generative modelling purposes. The decision on the representation used for the generative model is based on the trade-off between the accuracy of the formation energy model and the dimensionality of the system. Using a higher-dimensional representation might give better results, but would also make it harder to train our generative model. Therefore, we choose Rep2 for training the CVAE.