One particularly interesting use of these models, and our 487 previous CG models of 2D covalent organic frameworks, design. Our model opens up interesting questions about the role 527 of dynamics in controlled self-assembly; the balance between 528 achieving stability and control over the outcome of self-assembly 529 is not trivial. Low-cost computational tools can be used to 530 approach the prediction and design of less trivial cases involving 531 larger, or unsymmetrical ligands that are near-intractable to 532 study atomistically on large scales. We propose that this 533 will lead to much more efficient screening and design of cage 534 molecules as, for example, a step in approaches similar to that 535 by Young et al. subcomponent self-assembly), this iteration does not include 549 the necessary detail at the metal centres to properly encode 550 the twist and stereochemistry present in these MOCs. We 551 do not model the actual self-assembly process, which is likely 552 necessary to predict self-sorting outcomes generally, and our 553 approach will likely result in false positives. Additionally, it is 554 expected that many self-sorting outcomes can be overcome 555 by choice of experimental conditions. The effect and degree of flexibility in cage structures are likely to depend on the solvent and environment. Further, the nature of kinetic traps and stable intermediates in cage formation is not trivial to understand. Including higher resolution CG models (e.g., the Martini model 64 ) may help overcome inconsistencies in the phase space, especially for larger molecules. Indeed, to study interesting phenomena such as stability cliffs in the cage phase space, we require higher CG resolution. Understanding these sharp changes in stability or properties over a phase space, and whether they are topological effects, could be useful in controlling self-assembly in complex cage structures.