CNNs have been performed on global crystal structure and space group identification as summarized in Table . A recent notable entry is Corriero et al. who published their model with an online graphical user interface. . This follows the physical intuition that lower-symmetry space groups exhibit more peaks and require more independent parameters to describe their crystal structure. It is therefore imperative for symmetry prediction models that the classification performance and model certainty within each space group and crystal system should be reported to users and considered on a case-by-case basis. Unfortunately, the only certain solution to the data scarcity problem is to discover novel compounds in under-represented symmetries, a slow and tedious process. However, Schopmans et al. reported a method to pre-train a neural network on theoretical, yet-demonstrated crystal structures-of which there are infinitely many for each space group-achieving a minimum space group prediction accuracy of 80%. This strategy of pre-training or "transfer-learning" (transferring knowledge gained from one domain to another) is one of the under-explored topics of current literature.