For future work, a partial improvement of the predictions may be obtained by generating a new dataset where the topology optimization is run with more stringent convergence requirements to ensure that the final crystal structure is fully relaxed. In fact, for some of the crystal structures contained in the current dataset, convergence was not fully reached since the bulk modulus is more sensitive to small changes in geometry than the formation energy. Therefore, the fidelity of bulk modulus evaluations is lower than for the formation energy, which in turn limits the attainable accuracy of the HydraGNN model to predicting the former compared to the latter. Additional improvement in the predictions of the bulk modulus may also be obtained by including additional input features, both geometric features describing the overall crystal structure (e.g., angles) as well as chemical features for each atom species (e.g., valence electrons, electronegativity/electropositivity, atomic mass, atom size), fed into the HydraGNN model.