Use of an enhanced bulk diffusion-based algorithm for phase separation of a ternary mixture.

Area of Science:

• Condensed Matter Physics
• Materials Science
• Computational Physics

Background:

• Phase separation is crucial in materials science, but standard simulation methods struggle with late-stage dynamics.
• Studying ternary mixtures presents unique challenges due to increased complexity.
• Deep quenches require efficient algorithms to capture segregation processes.

Purpose of the Study:

• To develop and validate an accelerated Monte Carlo algorithm for simulating phase separation kinetics in 2D ternary mixtures.
• To investigate domain growth and scaling behavior across various quench depths.
• To compare the computational efficiency of the new accelerated algorithm against standard Kawasaki kinetics.

Main Methods:

• Utilized a Monte Carlo approach with an extended accelerated algorithm based on Marko and Barkema's work.
• Discretized the three-component system onto two coupled lattices.
• Analyzed domain growth and scaling behavior under deep quench conditions.

Main Results:

• The accelerated algorithm effectively simulates late-stage phase separation kinetics in 2D ternary systems.
• Domain growth and scaling behavior were studied over a broad range of quench depths.
• Demonstrated superior computational performance compared to standard Kawasaki kinetics for deep quenches.

Conclusions:

• The developed accelerated Monte Carlo algorithm is a powerful tool for studying complex phase separation dynamics.
• This method overcomes limitations of standard algorithms in deep quench scenarios.
• Provides insights into the fundamental processes governing segregation in multi-component systems.