Data Mining for Parameters Affecting Polymorph Selection in Contorted Hexabenzocoronene Derivatives

Hiszpanski, Anna M.; Dsilva, Carmeline J.; Kevrekidis, Ioannis G.; Loo, Yueh-Lin

doi:10.1021/acs.chemmater.8b00679

Data Mining for Parameters Affecting Polymorph Selection in Contorted Hexabenzocoronene Derivatives

Journal Article · Mon Apr 23 00:00:00 EDT 2018 · Chemistry of Materials

DOI:https://doi.org/10.1021/acs.chemmater.8b00679· OSTI ID:1491630

Hiszpanski, Anna M. ^[1]; Dsilva, Carmeline J. ^[2]; Kevrekidis, Ioannis G. ^[3]; ^[4]

Princeton Univ., NJ (United States). Dept. of Chemical and Biological Engineering; Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Materials Science Division
Princeton Univ., NJ (United States). Dept. of Chemical and Biological Engineering
Princeton Univ., NJ (United States). Dept. of Chemical and Biological Engineering; John Hopkins Univ., Baltimore, MD (United States). Dept. of Applied Mathematics and Statistics
Princeton Univ., NJ (United States). Dept. of Chemical and Biological Engineering; Princeton Univ., NJ (United States). Andlinger Center for Energy and the Environment

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. Finally, 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.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 1491630

Report Number(s):: LLNL-JRNL--739547; 892935

Journal Information:: Chemistry of Materials, Journal Name: Chemistry of Materials Journal Issue: 10 Vol. 30; ISSN 0897-4756

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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