Crystal-structure prediction via the floppy-box Monte Carlo algorithm: method and application to hard (non)convex...

Area of Science:

• Computational physics
• Materials science
• Colloid science

Background:

• Predicting crystal structures is crucial for understanding material properties.
• Colloidal particles offer a model system for studying phase behavior and self-assembly.
• Computational methods are essential for exploring complex particle interactions and structures.

Purpose of the Study:

• To provide a detailed guide for implementing the floppy-box Monte Carlo (FBMC) method.
• To enable crystal-structure prediction for systems with shape-anisotropic particles.
• To present results for dense crystal structures of faceted particles.

Main Methods:

• Detailed explanation of the floppy-box Monte Carlo (FBMC) algorithm.
• Special attention to handling hard-particle interactions and analogous soft interactions.
• Discussion of overlap-checking algorithms: separating axes and triangular tessellation.

Main Results:

• Successful prediction of dense crystal structures for 159 (non)convex faceted particles.
• Demonstration of FBMC's applicability to shape-anisotropic particle systems.
• Validation of the FBMC method for crystal structure prediction.

Conclusions:

• The FBMC method is a robust and implementable tool for colloidal crystal structure prediction.
• The method effectively handles various particle shapes and interaction potentials.
• This work provides a foundation for further simulations and theoretical studies in the field.