In future work, we hope to examine such possible improvements. One avenue is to explore different convolutions, such as our previous work. Another is to examine another type of graph representation, such as equivariant representation. The extent of polymorphism investigated here is also rather small; most polymorphic landscapes in a crystal structure prediction style workflow contain thousands if not tens of thousands of structures. Hence, we would also want to investigate the behavior of these models with more extensive polymorphic landscapes. On this note, it would be important to evaluate how these models perform on both absolute and relative ranking tasks in such a case. Supplementary information. Supplementary information is available wherein we show the parity/loss for different graph sizes in accordance with Taniguchi et al., as well as parity plots for fine-tuning attempts with transforms in torch geometric. We also share optimized crystal structures of the polymorphs of ROY and the entire PAH101 dataset as separate zip files.