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An Evolutionary Algorithm for the Discovery of Porous Organic Cages

submitted on 10.08.2018, 07:32 and posted on 10.08.2018, 12:08 by Enrico Berardo, Lukas Turcani, Marcin Miklitz, Kim Jelfs
The phase space of possible supramolecular materials is enormous, as they can, in principle, be built from any combination of organic building blocks. Here we have developed an evolutionary algorithm (EA) that can assist in the efficient exploration of chemical space for supramolecules, helping to guide synthesis to materials with promising applications. We demonstrate the utility of our EA to porous organic cages, predicting both promising targets and identifying the chemical features that emerge as important for a cage to be shape persistent or to adopt a particular cavity size. We identify that shape persistent cages require a low percentage of rotatable bonds in their precursors (<20%) and that the higher topicity building block in particular should use

double bonds for rigidity. We can use the EA to explore what size ranges for precursors are required for achieving a given pore size in a cage and show that 16 Å pores, which are absent in the literature, should be synthetically achievable. Our EA implementation is adaptable and easily extendable, not only to target specific properties of porous

organic cages, such as optimal encapsulants or molecular separation materials, but also to any easily calculable property of other supramolecular materials.


Email Address of Submitting Author


Imperial College London


United Kingdom

ORCID For Submitting Author


Declaration of Conflict of Interest