The use of the bond length as edge features allows the graph convolutional layers to weigh the information passed across neighboring nodes based on their Euclidean distance. This additional information helps better discriminate between different crystal structures, thus enhancing the predictive power of model for accurate predictions of both formation energy and bulk modulus. This fact is confirmed both by the values of the test MAE reported in Table , as well as the scatterplots provided in the Supplementary Material.