Computed crystal energy landscapes for understanding and predicting organic crystal structures and polymorphism

Area of Science:

• Solid-state chemistry
• Computational materials science
• Crystallography

Background:

• Polymorphism, the ability of a molecule to exist in multiple crystal structures, impacts material properties and manufacturing.
• Advances in computing power have enabled computational prediction of organic crystal structures.
• Crystal energy landscapes map potential polymorphs, revealing packing variations and energetic feasibility.

Purpose of the Study:

• To discuss the utility of calculating crystal energy landscapes as a complementary technique to experimental solid-form screening.
• To highlight how computed landscapes predict the complexity of organic solid states.
• To rationalize the occurrence of polymorphs based on computed low-energy structures.

Main Methods:

• Calculation of crystal energy landscapes to identify potential polymorphs.
• Comparison of computed crystal structures with experimentally observed forms.
• Analysis of packing, hydrogen bonding, and pi-pi stacking in energetically feasible structures.

Main Results:

• Computational methods can predict known crystal structures, particularly the most stable forms.
• Crystal energy landscapes often reveal a wide range of low-energy structures, including alternative polymorphs.
• Systems with numerous equi-energetic structures correlate with crystallization challenges.

Conclusions:

• Computed crystal energy landscapes provide valuable insights complementing experimental polymorph screening.
• Examination of low-energy computed structures aids in rationalizing observed crystalline forms.
• Understanding crystal energy landscapes is crucial for controlling crystallization and material properties.