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Discovery of Crystallizable Organic Semiconductors with Machine Learning.
Johnson, Holly M; Gusev, Filipp; Dull, Jordan T; Seo, Yejoon; Priestley, Rodney D; Isayev, Olexandr; Rand, Barry P.
Afiliación
  • Johnson HM; Department of Electrical and Computer Engineering, Princeton University, Princeton, New Jersey 08544, United States.
  • Gusev F; Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.
  • Dull JT; Department of Chemistry, Mellon College of Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.
  • Seo Y; Department of Electrical and Computer Engineering, Princeton University, Princeton, New Jersey 08544, United States.
  • Priestley RD; Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, United States.
  • Isayev O; Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, United States.
  • Rand BP; Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.
J Am Chem Soc ; 146(31): 21583-21590, 2024 Aug 07.
Article en En | MEDLINE | ID: mdl-39051486
ABSTRACT
Crystalline organic semiconductors are known to have improved charge carrier mobility and exciton diffusion length in comparison to their amorphous counterparts. Certain organic molecular thin films can be transitioned from initially prepared amorphous layers to large-scale crystalline films via abrupt thermal annealing. Ideally, these films crystallize as platelets with long-range-ordered domains on the scale of tens to hundreds of microns. However, other organic molecular thin films may instead crystallize as spherulites or resist crystallization entirely. Organic molecules that have the capability of transforming into a platelet morphology feature both high melting point (Tm) and crystallization driving force (ΔGc). In this work, we employed machine learning (ML) to identify candidate organic materials with the potential to crystallize into platelets by estimating the aforementioned thermal properties. Six organic molecules identified by the ML algorithm were experimentally evaluated; three crystallized as platelets, one crystallized as a spherulite, and two resisted thin film crystallization. These results demonstrate a successful application of ML in the scope of predicting thermal properties of organic molecules and reinforce the principles of Tm and ΔGc as metrics that aid in predicting the crystallization behavior of organic thin films.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Am Chem Soc Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Am Chem Soc Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos