The message passing policy used to transfer information across nodes in the graph significantly impacts the predictive accuracy of the HydraGNN model. The MAEs provided in Table and the scatterplots included in the Supplementary Material show that PNAConv outperforms CGConv for both formation energy and bulk modulus, because the PNA better preserves graph isomorphism and thus better distinguishes different atomic disordered configurations.