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Dynamic Monte Carlo versus Brownian dynamics: A comparison for self-diffusion and crystallization in colloidal fluids
1SUPA, School of Physics, University of Edinburgh, Mayfield Road, Edinburgh EH9 3JZ, Scotland. esanz@ph.ed.ac.uk
The Journal of Chemical Physics
|May 27, 2010
Summary
Rescaling Monte Carlo time by acceptance probability improves agreement with Brownian dynamics simulations for colloidal systems. This method enhances accuracy for diffusion coefficients and crystallization times, offering a practical recipe for simulations.
Area of Science:
- Computational Physics
- Colloid Science
- Statistical Mechanics
Background:
- Accurate simulation of colloidal systems is crucial for understanding their behavior.
- Dynamic Monte Carlo (DMC) and Brownian dynamics (BD) are common simulation methods.
- Differences in time-scaling can lead to discrepancies between DMC and BD results.
Purpose of the Study:
- To comparatively study dynamic Monte Carlo and Brownian dynamics simulations.
- To investigate the effect of time-rescaling in DMC on simulation outcomes.
- To establish a theoretical basis for a practical DMC simulation recipe.
Main Methods:
- Comparative simulation of colloidal systems with repulsive interactions using DMC and BD.
- Rescaling Monte Carlo time by the acceptance probability.
- Analysis of a single particle in a one-dimensional potential.
Main Results:
- Rescaling DMC time by acceptance probability yields good agreement for self-diffusion coefficients compared to BD.
- Rescaled DMC provides fair agreement for crystallization times with BD simulations.
- Faster convergence of DMC to BD results is observed when time is rescaled by acceptance probability.
Conclusions:
- Time-rescaling of DMC simulations using acceptance probability is a valid and effective method.
- This approach bridges the gap between DMC and BD simulation results for colloidal systems.
- The findings provide a theoretical justification for a practical and accurate simulation technique.
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