Rationalising and controlling the formation of different polymorphs of organic molecular crystals is a frontier challenge spanning computational and synthetic chemistry. Importantly, different polymorphs of a molecular material can exhibit vastly different properties, such as melting point, light absorption and solubility. These difference in turn have important implications in areas spanning from drug development in the traditional pharmaceutical industry to organic solar material design.

Here, we propose to develop and test an entirely new computational strategy for predicting the kinetic pathways that form different polymorphs. Specifically, this project aims to adapt our well-established graph-driven sampling (GDS) strategy to enable generation of intermediate ensembles associated with nucleation and crystallization to different polymorphs. GDS is a `directed walk’ strategy that operates by seeking out pathways connecting defined end-point structures – repeated GDS simulations can rapidly build up a picture of the landscape of intermediates that lie along the pathways connecting these end-points, providing detailed insight into competing mechanisms. GDS has to date been used to generate large-scale chemical reaction networks associated with catalysis, organic synthesis and interstellar chemistry - and has most recently been adapted to study the challenging problem of protein-folding pathway generation.

Hypothesis and objectives

Building on these varied proof-of-concepts, we hypothesise that GDS can equally be adapted to study crystallization/nucleation mechanisms towards different polymorphs; this project aims to deliver and validate this new strategy, as well as explore how information about kinetics can be used to enhance reaction-coordinate identification in polymorph prediction.

Anticipated objectives are: