Data Mining for Parameters Affecting Polymorph Selection in Contorted Hexabenzocoronene Derivatives

The
macroscopic properties of molecular materials can be drastically
influenced by their solid-state packing arrangements, of which there
can be many (e.g., polymorphism). Strategies to controllably and predictively
access select polymorphs are thus highly desired, but computationally
predicting the conditions necessary to access a given polymorph is
challenging with the current state of the art. Using derivatives of
contorted hexabenzocoronene, cHBC, we employed data mining, rather
than first-principles approaches, to find relationships between the
crystallizing molecule, postdeposition solvent-vapor annealing conditions
that induce polymorphic transformation, and the resulting polymorphs.
This analysis yields a correlative function that can be used to successfully
predict the appearance of either one of two polymorphs in thin films
of cHBC derivatives. Within the postdeposition processing phase space
of cHBC derivatives, we have demonstrated an approach to generate
guidelines to select crystallization conditions to bias polymorph
access. We believe this approach can be applied more broadly to accelerate
the predictions of processing conditions to access desired molecular
polymorphs, making progress toward one of the grand challenges identified
by the Materials Genome Initiative.