The architecture of the GCNN model has 10 graph convolutional layers with 100 neurons per layer. At first, we selected a radius cutoff of 5 Å to build the local neighborhoods used by the graph convolutional mask. We attach two FC layers at the end of the stack of convolutional layers, with 50 neurons in the first FC layer and 25 neurons in the second FC layer. Using the same GCNN architecture, two separate training procedures are performed, one to learn the formation energy and one to learn the bulk modulus. The GCNN models are trained using the Adam method with a learning rate equal to 0.001, batch sizes of 32, and a maximum number of epochs set to 200. Early stopping is performed to interrupt the training when the validation loss function does not decrease for several consecutive epochs, as this is a symptom indicating that further epochs are very unlikely to reduce the value of the loss function. The training set for each of the NN represents 70% of the total dataset; the remaining portion of the data is equally split between validation and testing. As discussed in Section 3, compositional stratified splitting was performed to ensure that all the compositions were equally represented across training, validation, and testing datasets. The training of each DL model was performed on an NVIDIA V100 GPU.