See Sec. S7.1 for more discussion. A third limitation is that, while we may aim for the singular value decomposition to learn distinguishing features of the cavities of the cages † , the algorithm takes notice of the moieties protruding from the core in addition to the internal cavity. This is evident by e.g. the distance between C9 and C1 and RCC1a and RCC1c in Fig. . A method to incentivize a dimensionality reduction algorithm to pay more attention to the center of the cage may be warranted if the internal cavity is the most important feature to describe. That said, the encoding of the periphery of the molecule may be desirable for e.g. predicting how cage molecules assemble to form the solid. The final limitation is that the singular value decomposition is sensitive to alignments and translations for inter-cage comparisons. One's mathematical definition of "optimal alignment" (e.g. using rotational dynamics or Coherent Point Drift) may not coincide with one's intuition of how the cages should be aligned. Our future work is to employ rotational-invariant algorithms that detect features of cavities regardless of their orientation.