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...
| Veröffentlicht in: | Chemistry of materials : a publication of the American Chemical Society. - 1998. - 30(2018), 10 vom: 22. Mai, Seite 3330-3337 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2018
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| Zugriff auf das übergeordnete Werk: | Chemistry of materials : a publication of the American Chemical Society |
| Schlagworte: | Journal Article |
| Zusammenfassung: | 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 |
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| Beschreibung: | Date Revised 25.02.2020 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 0897-4756 |
| DOI: | 10.1021/acs.chemmater.8b00679 |