High Throughput Profiling of Molecular Shapes in Crystals

Area of Science:

• Crystallography
• Computational Chemistry
• Materials Science

Background:

• Molecular shape is crucial for crystallization and supramolecular assembly, but its precise role remains unclear.
• Understanding molecular shape in crystalline environments is essential for predicting material properties and designing new structures.

Purpose of the Study:

• To present a computationally efficient method for describing and classifying molecular shapes within crystal structures.
• To demonstrate the method's applicability across various chemical systems, from metals to complex organic molecules.

Main Methods:

• Utilizing rotation-invariant descriptions of Hirshfeld surfaces via spherical harmonic functions.
• Applying principal component analysis and cluster analysis to classify molecular shapes.
• Validating the method on a large dataset of over 14,000 crystal structures from the Cambridge Structural Database (CSD).

Main Results:

• The method successfully classified metal crystal structures based on their lattice types.
• Classification of organic crystal structures revealed groupings based on chemical scaffolds, isosterism, and conformational similarity.
• Demonstrated computational efficiency suitable for high-throughput screening.

Conclusions:

• The developed method provides an effective tool for analyzing and classifying molecular shapes in crystals.
• This approach has significant implications for drug discovery, supramolecular chemistry, and the design of novel materials.
• Enables large-scale screening of molecular shapes and interactions within existing crystallographic databases.