Data Mining for Parameters Affecting Polymorph Selection in Contorted Hexabenzocoronene Derivatives

Abstract

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.

Coronenes, Benzocoronenes and Beyond: Modern Aspects of Their Syntheses, Properties, and Applications

“…This packing motif may benefit the electronic coupling and thus FET mobility [211–212] . Nuckolls group and others made a detailed study of the properties of the parent [213] and substituted [214] cata ‐HBCs, showing different self‐assemblies based on the different intermolecular associations. For example, strong F…F intermolecular interactions were observed in the self‐assembly of fluorinated cata ‐HBCs [215] .…”

Computationally aided design of a high-performance organic semiconductor: the development of a universal crystal engineering core

“…The as-cast lms were also subjected to post-deposition thermal annealing (TA) and solvent vapor annealing (SVA) 71 to assess polymorphic stability, as these techniques have demonstrated the ability to induce polymorphic transformations in organic small molecules. [72][73][74] 3b-h is crystalline as-deposited by both spin-casting ( Fig. 5a) and aligned drop-casting (Fig.…”

Autonomous Discovery in the Chemical Sciences Part I: Progress

“…For hypothesized metallic alloys, for example, one can predict crystal structures [397–400] and phase behavior [401–406] . For organic molecules, one can similarly predict whether compounds are likely to crystallize easily [407] and their preferred processing‐dependent polymorph [408] . Machine learning models can also reduce the number of evaluations required, for example, for finding minimum energy configurations [399] …”