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Data Mining for Parameters Affecting Polymorph Selection in Contorted Hexabenzocoronene Derivatives.

Anna M Hiszpanski1,2, Carmeline J Dsilva1, Ioannis G Kevrekidis1,3

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|June 11, 2019
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Summary
This summary is machine-generated.

Researchers developed a data-driven method to predict and control the solid-state packing of molecular materials. This approach uses data mining to guide the selection of crystallization conditions, enabling targeted access to desired polymorphs.

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Area of Science:

  • Materials Science
  • Crystallography
  • Computational Chemistry

Background:

  • Solid-state packing arrangements significantly influence molecular material properties.
  • Controlling polymorphism (multiple crystal structures) is crucial but computationally challenging.
  • Current methods struggle to predict conditions for accessing specific polymorphs.

Purpose of the Study:

  • To develop a predictive approach for controlling polymorph selection in molecular materials.
  • To establish relationships between molecular structure, processing conditions, and resulting polymorphs.
  • To accelerate the discovery of targeted crystallization pathways.

Main Methods:

  • Utilized data mining on contorted hexabenzocoronene (cHBC) derivatives.
  • Analyzed relationships between molecular structure, solvent-vapor annealing conditions, and polymorph formation.
  • Developed a correlative function for polymorph prediction.

Main Results:

  • Successfully predicted the appearance of specific polymorphs in cHBC derivative thin films.
  • Generated guidelines for selecting crystallization conditions to bias polymorph access.
  • Demonstrated a data-driven approach for controlling solid-state packing.

Conclusions:

  • The data mining approach effectively predicts and guides polymorph selection.
  • This method accelerates the identification of processing conditions for desired molecular polymorphs.
  • The approach aligns with Materials Genome Initiative goals for predictive materials design.