The G‐JF Thermostat for Accurate Configurational Sampling in Soft‐Matter Simulations

Arad, Evyatar; Farago, Oded; Grønbech‐Jensen, Niels

doi:10.1002/ijch.201500067

Cited by 15 publications

(13 citation statements)

References 19 publications

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“…To test the robustness of the new velocity variables beyond the harmonic oscillator test case, we consider the non-linear model presented in ref. [11] of a particle moving in a one-dimensional potential U (r) = kr 2 /2−cos(r− ξ), with k = 1/40, and ξ = 3/4π [see fig. 1(a)].…”

Section: Simulations Of a Non-linear Modelmentioning

confidence: 99%

“…There exist other thermostats that reproduce the kinetic energy without discretization errors, but no existing algorithm has simultaneously both the correct kinetic and potential energy of the harmonic oscillator. Since the aim of computer simulation studies of molecular systems at equilibrium is phase space sampling, the velocity variable is essentially an auxiliary field and one should favor the use of algorithms like the GJF thermostat, which have been demonstrated to provide robust configurational sampling not only for the harmonic oscillator but also for non-linear molecular systems [10,11]. Nevertheless, the kinetic energy constitutes a useful and a simple measure for the temperature of the system and, therefore, a question arises on whether it is possible to devise a thermostat featuring both correct potential and kinetic energies of the harmonic oscillator.…”

Section: Introductionmentioning

confidence: 99%

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Langevin thermostat for robust configurational and kinetic sampling

Farago

2019

Physica A: Statistical Mechanics and its Applications

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We reformulate the algorithm of Grønbech-Jensen and Farago (GJF) for Langevin dynamics simulations at constant temperature. The GJF algorithm has become increasingly popular in molecular dynamics simulations because it provides robust (i.e., insensitive to variations in the time step) and accurate configurational sampling of the phase space with larger time steps than other Langevin thermostats. In the original derivation [Mol. Phys. 111, 983 (2013)], the algorithm was formulated as a velocity-Verlet type integrator with an in-site velocity variable. Here, we reformulate it as a leap frog scheme with a half-step velocity variable. In contrast to the original form, the reforumlated one also provides robust and accurate estimations of kinetic measures such as the average kinetic energy. We analytically prove that the newly presented algorithm gives the exact configurational and kinetic temperatures of a harmonic oscillator for any time step smaller than the Verlet stability limit, and use computer simulations to demonstrate the configurational and kinetic robustness of the algorithm in strongly non-linear systems. This property of the new formulation of the GJF thermostat makes it very attractive for implementation in computer simulations.

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Section: Simulations Of a Non-linear Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Langevin thermostat for robust configurational and kinetic sampling

Farago

2019

Physica A: Statistical Mechanics and its Applications

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“…We integrate Eq. (2) numerically by a thermodynamically sound variant of the velocity-explicit Størmer-Verlet algorithm [12] with a normalized time step t = 0.02, which is far below the stability limit of the integrator and well within the range of the time steps that produce statistically reliable simulation data for this problem and this method [13].…”

Section: Model and Numerical Approachmentioning

confidence: 99%

Nonequilibrium transient phenomena in the washboard potential

et al. 2018

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Transient properties of the one-dimensional washboard potential are investigated in order to understand observed modulations in the statistics of escape events. Specifically, we analyze the effects of different kinds of initial conditions on the escape distribution obtained by linearly increasing the tilt of the potential. Despite the complexity of the dynamics leading up to the eventual escape, we find that the overall statistics can be interpreted in terms of the system parameters, which offers illuminating perspectives for driven one-dimensional systems with washboard potentials. We choose parameters sets relevant for Josephson junctions, a commonly studied system due to both its applications and its use as a model system in condensed matter physics.

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“…A key question that arises then is: how large can the time step h be made without introducing unacceptable levels of error into averaged static and dynamic quantities? In [4], G-JF is used to simulate a CG lipid bilayer in implicit solvent and averaged energy terms (both potential and kinetic) are examined: however, the system was simulated for relatively short MD trajectories (<50,000 steps) and distributions were not examined. Also included in that study was the Schneider-Stoll Langevin integrator [32], the default option in LAMMPS [29] and ESPResSo [20].…”

Section: Introductionmentioning

confidence: 99%

Comparison of modern Langevin integrators for simulations of coarse-grained polymer melts

2019

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For a wide range of phenomena, current computational ability does not always allow for fully atomistic simulations of high-dimensional molecular systems to reach time scales of interest. Coarse-graining (CG) is an established approach to alleviate the impact of computational limits while retaining the same algorithms used in atomistic simulations. It is of importance to understand how algorithms such as Langevin integrators perform on nontrivial CG molecular systems, and in particular how large of an integration time step can be used without introducing unacceptable amounts of error into averaged quantities of interest. To investigate this, we examined three different Langevin integrators on a CG polymer melt: the recently developed BAOAB method by Leimkuhler and Matthews [17], the Grønbech-Jensen and Farago method [12], or G-JF, and the frequently used Brünger-Brooks-Karplus integrator [6], also known as BBK. We compute and analyze key statistical properties for each. Our results indicate that the three integrators perform similarly when using a small friction parameter; however, outside of this regime the use of large integration steps produces significant deviations from the predicted diffusivity and steady-state distributions for all integration methods examined with the exception of G-JF.2010 Mathematics Subject Classification. 82C31; 82C80; 82D15.

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The G‐JF Thermostat for Accurate Configurational Sampling in Soft‐Matter Simulations

Cited by 15 publications

References 19 publications

Langevin thermostat for robust configurational and kinetic sampling

Langevin thermostat for robust configurational and kinetic sampling

Nonequilibrium transient phenomena in the washboard potential

Comparison of modern Langevin integrators for simulations of coarse-grained polymer melts

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