Efficient method for predicting crystal structures at finite temperature: variable box shape simulations.

Area of Science:

• Computational materials science
• Statistical mechanics
• Crystallography

Background:

• Predicting crystal structures at finite temperatures is crucial for understanding material properties.
• Existing methods can be computationally expensive or limited in scope.
• Accurate structure prediction requires handling diverse interaction potentials.

Purpose of the Study:

• To develop an efficient and robust computational method for crystal structure prediction.
• To validate the method across a range of interaction types and system complexities.
• To explore new crystal structures and phase behavior for specific particle systems.

Main Methods:

• Utilizing Monte Carlo simulations for structure prediction at finite temperatures.
• Incorporating Ewald sums for accurate treatment of long-range interactions.
• Performing full free energy calculations to construct phase diagrams.

Main Results:

• Demonstrated effectiveness for hard, attractive, anisotropic, and soft interactions.
• Accurate prediction of crystal structures for binary hard-sphere mixtures, star polymers, and binary Lennard-Jones mixtures, consistent with literature.
• Prediction of novel crystal structures for hard asymmetric dumbbell particles, bowl-like particles, and hard oblate cylinders.
• Construction of the phase diagram for hard oblate cylinders.

Conclusions:

• The presented Monte Carlo method is efficient, robust, and versatile for crystal structure prediction.
• The method provides reliable predictions across various interaction types and system complexities.
• New crystal structures and phase behavior have been identified for several particle systems, advancing materials discovery.