We conducted a series of "ablation studies" (one parameter is changed at a time) to monitor the change in predictive performance of our HydraGNN model with respect to the most important hyperparameters: policy of message passing performed by the graph convolutional layers, bond length as edge feature, and radius cut-off to define the local environment for information gathering. The mean absolute error (MAE) obtained by the HydraGNN models on the test dataset for predictions of both formation energy and bulk modulus are reported in Table . For both formation energy and bulk modulus, the computational time for training was on average 25,700 wall-clock seconds.