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Predicting molecular crystal polymorphs is crucial for drug development. This study combines advanced sampling and first-principles ranking for accurate energy landscape prediction, improving the reliability of identifying potential drug polymorphs.

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Area of Science:

  • Computational chemistry
  • Materials science
  • Crystallography

Background:

  • Predicting molecular crystal polymorphs is vital for drug development, particularly for identifying late-appearing polymorphs.
  • Computational challenges arise from high-dimensional conformational and crystallographic spaces and the need for high-accuracy energy calculations (1 kJ/mol).

Purpose of the Study:

  • To develop a reliable computational method for predicting the polymorphic energy landscape of molecular crystals.
  • To refine relative stabilities in crystal structure prediction using a hierarchical energy ranking approach.

Main Methods:

  • Integration of a leading crystal structure sampling strategy with a top-performing first-principles energy ranking strategy.
  • Application of a hierarchical energy ranking approach for final-stage stability refinement in crystal structure prediction.

Main Results:

  • The combined approach achieved excellent stability rankings for all investigated systems.
  • Demonstrated the efficacy of the hierarchical energy ranking for refining relative stabilities.

Conclusions:

  • The developed combined approach enhances the accuracy of predicting molecular crystal polymorphs.
  • This method is applicable to molecular crystals of pharmaceutical importance, aiding in drug development.