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Nonequilibrium Monte Carlo simulation for a driven Brownian particle.

¹School of Chemistry F11, University of Sydney, New South Wales 2006, Australia.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|November 13, 2009

Summary

This study validates a nonequilibrium probability distribution for time-varying mechanical work. The nonequilibrium Monte Carlo method successfully simulated a driven Brownian particle, confirming the distribution

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Area of Science:

Statistical Mechanics
Computational Physics

Background:

The study of systems not in thermal equilibrium is crucial for understanding complex phenomena.
Characterizing mechanical work in dynamic, nonequilibrium systems presents significant theoretical and computational challenges.

Purpose of the Study:

To test the validity of a nonequilibrium probability distribution for describing time-varying mechanical work.
To extend the applicability of the Nonequilibrium Monte Carlo (NEMC) algorithm to dynamic nonequilibrium systems.

Main Methods:

Simulating a Brownian particle in a soft-sphere solvent using the Nonequilibrium Monte Carlo (NEMC) algorithm.
Applying a moving external potential to drive the Brownian particle across various frequencies, from steady-state to transient regimes.

Main Results:

The NEMC algorithm was successfully applied to a time-varying nonequilibrium system.
Phase lag and amplitude data were obtained for a range of drive frequencies.
Results demonstrated agreement with nonequilibrium stochastic molecular dynamics and Nosé-Hoover molecular dynamics.

Conclusions:

The nonequilibrium probability distribution accurately describes time-varying mechanical work.
The NEMC method is a viable approach for simulating such dynamic systems.
The validated distribution provides a fundamental basis for nonequilibrium statistical mechanics.