Porous organic cages (POCs) are discrete molecules which contain intrinsic porosity. The packing of POCs is determined by weak dispersion forces, resulting in POC solids whose structure can be controlled by varying the chemical functionality or solvent molecules, vastly affecting their material properties.1 Over the past decade, there has been an increased interest in POCs2 but characterising their crystalline form often proves tricky.3 This is partly due to the complexity of predicting and rationalising their crystal structure through computational techniques, making targeted design difficult.4 Currently, state-of-the-art computational efforts focus on crystal structure prediction. However, the weak intermolecular interactions present in POCs result in a high number of possible low-energy polymorphs and, coupled with the high computational cost of these calculations, restrict the ability to use high-throughput approaches.6 In this study, we aim to devise a novel way of predicting the solid-state phase behaviour of POCs, elucidating design rules for new functional organic molecules by coarse-graining their degrees of freedom, representing the cages as hard particles with directional interactions between favoured motifs. We start by creating a coarse-grained model containing the dominant intermolecular interactions known to exist between cages, and employ Monte Carlo simulations to determine the thermodynamic phase behaviour of our model. Our results show that by manipulating the parameters of our model, we can reproduce the phase space spanned by octahedral POCs present in the crystal structure databases. By mapping the coarse-grained phase space back onto intermolecular interaction strengths determined by DFT calculations and force field models, we can directly relate each cage to its likely crystal packing structure, highlighting the potential for this model to predict the packing of new, or uncharacterized, cages. Moreover, our results highlight new phases which are unreported within the literature. The workflow set out in this study can help predict the likely structure of new porous organic materials with markedly less computational expense than current techniques, increasing the capability for high throughput approaches.

Coarse-grained modelling to predict the packing of porous organic cages

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