On modelling disordered crystal structures through restraints from molecule-in-cluster computations, and...

Area of Science:

• Crystallography and Materials Science
• Solid-State Chemistry

Background:

• Current methods for distinguishing static and dynamic disorder in crystal structures rely on multi-temperature diffraction experiments, offering descriptive but not predictive insights.
• Existing disorder models often lack predictive power and can be complex to refine using conventional least-squares methods.

Purpose of the Study:

• To develop a predictive approach for classifying crystal structure disorder as static or dynamic.
• To improve the refinement of disordered crystal structures by integrating optimized 'archetype structures'.

Main Methods:

• Utilized the molecule-in-cluster approach to optimize separately identified conformations from existing disorder models.
• Recombined these optimized 'archetype structures' using restrained positional and constrained displacement parameters for refinement.
• Analyzed ten example structures and performed further computations for disorder classification.

Main Results:

• The proposed method achieved a superior fit to experimental diffraction data compared to traditional methods relying solely on experimental information.
• Energy differences between distinct disorder conformations were typically found within a small energy window of RT (where T is the crystallization temperature).
• Successfully classified disorder into static or dynamic types using single experiments at one temperature, demonstrated with propionamide.

Conclusions:

• The molecule-in-cluster approach offers a standardized and simplified method for disorder refinement in crystallography.
• This new methodology provides a predictive framework for understanding crystal structure disorder, advancing beyond purely descriptive analyses.
• The findings enable the classification of disorder from single-temperature experiments, offering significant advantages in efficiency and predictive capability.