The CGCNN architecture was varied with 8, 16, 32 latent dimensions in the last layer and trained on the two property sets. The 32-dimensional latent space was chosen as providing the optimal training to testing performance across the QMOF database in both cases. The CGCNN feature set therefore consists of 292 features, nine sets of 32 features each set representing band gap, total energy, shear modulus, bulk modulus, Poisson ratio, Fermi energy, formation energy and energy per atom influencers in structure respectively. On top there are additional features in terms of final predicted total energy, band gap for the material.