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Adaptive stochastic methods for sampling driven molecular systems.

Andrew Jones1, Ben Leimkuhler

  • 1School of Physics, University of Edinburgh EH9 3JZ, United Kingdom.

The Journal of Chemical Physics
|September 8, 2011
PubMed
Summary
This summary is machine-generated.

New thermostatting methods generalize Nosé-Hoover and Langevin dynamics to maintain canonical sampling under stochastic forces. These adaptive techniques dissipate excess heat while preserving system ergodicity, even with nonlinear perturbations.

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Area of Science:

  • Statistical mechanics
  • Computational physics

Background:

  • Canonical sampling is crucial for simulating systems in equilibrium.
  • Driving stochastic forces introduce challenges in maintaining canonical distributions.
  • Existing methods may struggle with unknown perturbation strengths and nonlinearities.

Purpose of the Study:

  • To develop generalized thermostatting methods for canonical sampling under stochastic driving forces.
  • To enable adaptive dissipation of excess heat without prior knowledge of perturbation strength.
  • To ensure the preservation of ergodicity in driven systems.

Main Methods:

  • Generalization of the Nosé-Hoover method.
  • Adaptation of Langevin dynamics.
  • Numerical experiments to validate the methods.

Main Results:

  • The proposed methods effectively dissipate excess heat from steady Brownian perturbations.
  • Ergodicity is preserved even with nonlinear driving forces.
  • Adaptive control of the target canonical ensemble was demonstrated.

Conclusions:

  • The developed thermostatting methods offer robust canonical sampling in the presence of complex stochastic forces.
  • These techniques are adaptable and effective for nonlinear driving perturbations.
  • The findings advance computational approaches for simulating driven systems.