Displaying similar documents to “Theoretical and numerical comparison of some sampling methods for molecular dynamics”

A Metropolis adjusted Nosé-Hoover thermostat

Benedict Leimkuhler, Sebastian Reich (2009)

ESAIM: Mathematical Modelling and Numerical Analysis

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We present a Monte Carlo technique for sampling from the canonical distribution in molecular dynamics. The method is built upon the Nosé-Hoover constant temperature formulation and the generalized hybrid Monte Carlo method. In contrast to standard hybrid Monte Carlo methods only the thermostat degree of freedom is stochastically resampled during a Monte Carlo step.

Combining stochastic and deterministic approaches within high efficiency molecular simulations

Bruno Escribano, Elena Akhmatskaya, Jon Mujika (2013)

Open Mathematics

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Generalized Shadow Hybrid Monte Carlo (GSHMC) is a method for molecular simulations that rigorously alternates Monte Carlo sampling from a canonical ensemble with integration of trajectories using Molecular Dynamics (MD). While conventional hybrid Monte Carlo methods completely re-sample particle’s velocities between MD trajectories, our method suggests a partial velocity update procedure which keeps a part of the dynamic information throughout the simulation. We use shadow (modified)...

Molecular Simulation in the Canonical Ensemble and Beyond

Zhidong Jia, Ben Leimkuhler (2007)

ESAIM: Mathematical Modelling and Numerical Analysis

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In this paper, we discuss advanced thermostatting techniques for sampling molecular systems in the canonical ensemble. We first survey work on dynamical thermostatting methods, including the Nosé-Poincaré method, and generalized bath methods which introduce a more complicated extended model to obtain better ergodicity. We describe a general controlled temperature model, () and demonstrate that it flexibly accommodates existing alternative thermostatting methods, such as Nosé-Poincaré,...

Towards effective dynamics in complex systems by Markov kernel approximation

Christof Schütte, Tobias Jahnke (2009)

ESAIM: Mathematical Modelling and Numerical Analysis

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Many complex systems occurring in various application share the property that the underlying Markov process remains in certain regions of the state space for long times, and that transitions between such metastable sets occur only rarely. Often the dynamics within each metastable set is of minor importance, but the transitions between these sets are crucial for the behavior and the understanding of the system. Since simulations of the original process are usually prohibitively expensive,...