We employed a pretrained graph neural network, SchNet, on electronic energy of the QM9 dataset. QM9 is a set of 134K small-sized organic molecules (∼ 5-10 Å in size) with optimized conformations all computed using the B3LYP/6-31G(d,p) level of density-functional theory. For the SchNet GNN model, the nodal features (i.e the atom-embeddings) were chosen to have a 128-dimensional latent feature space. The edges that update the nodal features employ an initial expansion of interatomic distances using equally separated Gaussians with a cutoff of 50 Å. This provides the edges with enough parametric flexibility to update the nodal features via convolutional operation described in ref . More details on the GNN algorithm can also be found in refs . Note that more efficient GNN training algorithms employ cutoff distances that are significantly shorter which allow for efficiently learning the neighbor interactions. However, this avenue was not chosen since a large cutoff distance is purposeful to maintain a global representation of molecules in the embedding space. We trained on 100K molecules with total electronic energy at 0 Kelvin as the target property. An additional 10,000 data points were used for validation during the training process. The rest of the set (20,000) was leftover for testing. The trained model achieves a MAE of 0.2 meV and 1 meV on training and testing set's molecular energy, respectively. The trained model, details on all the hyperparameters, as well as the extracted embeddings for QM9 molecules can be found at .