Abstract
Crystal structure prediction methods can enable the in silico design of functional molecular crystals, but solvent effects can have a major influence on relative lattice energies, sometimes thwarting predictions. This is particularly true for porous solids, where solvent included in the pores can have an important energetic contribution. We present a Monte Carlo solvent insertion procedure for predicting the solvent filling of porous structures from crystal structure prediction landscapes, tested using a highly solvatomorphic porous organic cage molecule, CC1. Using this method, we can understand why the predicted global energy minimum structure for CC1 is never observed from solvent crystallisation. We also explain the formation of three different solvatomorphs of CC1 from three structurally-similar chlorinated solvents. Calculated solvent stabilisation energies are found to correlate with experimental results from thermogravimetric analysis, suggesting a future computational framework for a priori materials design that factors in solvation effects.
Highlights
A fundamental goal for computational chemistry is the in silico design of functional materials
Crystal structure prediction methods can enable the in silico design of functional molecular crystals, but solvent effects can have a major influence on relative lattice energies, sometimes thwarting predictions
We present a Monte Carlo solvent insertion procedure for predicting the solvent filling of porous structures from crystal structure prediction landscapes, tested using a highly solvatomorphic porous organic cage molecule, CC1
Summary
A fundamental goal for computational chemistry is the in silico design of functional materials. Because materials properties are de ned by structure, in silico methods for screening hypothetical functional molecules must involve a structural hypothesis; this can be done using analogy, Paper empirical rules, or ab initio solid-state structure prediction. A small change in molecular structure o en alters the crystal structure completely. This means that intuitive design strategies for molecular crystals will frequently fail or, at least, fail to capture the true complexity of the potential crystallisation landscape for a given molecule. Computational methods developed for crystal structure prediction (CSP) give us the potential to predict structure from the molecular building blocks alone.[1,2] So far, CSP methods have largely focused on pharmaceutical molecules,[3,4,5] but they have been applied in areas of materials chemistry, such as organic semiconductors[6,7,8,9] and porous organic molecular crystals.[10,11,12,13]
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