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Assessing crystallisation behaviour in molecular crystals through particle rugosities.

Marta Brocca1, Dominic Evans1, Helen Blade2

  • 1Department of Chemistry, University of Durham, Durham, UK.

Communications Chemistry
|June 29, 2026
PubMed
Summary

We developed a new method to calculate crystal surface rugosity, a key factor in crystal formation. Lower rugosity generally indicates crystals that are easier to nucleate and grow, aiding in the identification of experimentally accessible forms.

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Area of Science:

  • Crystallography
  • Materials Science
  • Computational Chemistry

Background:

  • Surface properties critically influence molecular crystal nucleation, growth, and crystallization.
  • Existing surface rugosity metrics are limited by incomplete surface topology descriptions.

Purpose of the Study:

  • To introduce a novel computational workflow for calculating surface descriptors across crystal facets.
  • To establish a new particle rugosity metric based on the Surface Area Ratio (SAR) for improved crystal analysis.

Main Methods:

  • Developed a workflow to compute surface descriptors for crystal facets.
  • Utilized the Surface Area Ratio (SAR) definition for surface rugosity.
  • Calculated average particle rugosity using three offset selection criteria for diverse crystallization conditions.

Main Results:

  • Analyzed particle rugosities for polymorphs, CSD datasets, and crystal structure prediction (CSP) landscapes.
  • Observed that lower rugosity generally correlates with enhanced nucleation and growth.
  • Demonstrated the metric's ability to differentiate experimentally accessible polymorphs from elusive ones.

Conclusions:

  • Particle rugosity serves as a valuable descriptor for crystal nucleation and growth.
  • This metric can aid in predicting and classifying the experimental feasibility of computationally generated polymorphs.
  • The findings support the use of rugosity as a complementary tool in crystal engineering and discovery.