Machine learning (ML) offers a readily available framework to fit models within a multidimensional S-C space, but a definition of this space is still required. For crystalline materials in particular, this presents a problem. The standard way of representing structures based on a unit cell and atomic coordinates is not a suitable construction of S-C space; the infinite possible unit cell definitions for the same material result in a non-unique representation, and small atomic shifts can result in discontinuous changes in unit cell metric (e.g. by forming supercells). To overcome this limitation, it is common to use crystal descriptors (also known as features) that represent a crystal structure in a unique way to allow direct comparisons. A range of descriptors exist for both organic and inorganic materials but are often either specialised to certain problems, do not scale to different numbers of atoms or are limited to non-standard ML architectures (such as graph-based methods). While many of these descriptors combine structural and compositional information together, separating them would allow a more flexible approach to constructing S-C space depending on the problem in hand, for instance emphasising structure over composition.