While we chose to focus on node density, incorporation of pymatgen charge decoration (Composition module) can help with smartly choosing radii for elements based on their covalent, ionic, and metallic radii for the hard sphere and probability density models and may offer better quality information for data science models. We hope to see this dataset used in crystallographic orientation dependent applications such as the Open Catalyst Project in pursuit of surrogate models for computationally complex physics-based simulations. iucr Fig. . Machine learning predictions of bulk modulus using only elemental presence as nominal data. Compounds with less than 6 elements were zero-padded.