Efficient multiple time scale molecular dynamics: using colored noise thermostats to stabilize resonances

Abstract

Multiple time scale molecular dynamics enhances computational efficiency by updating slow motions less frequently than fast motions. However, in practice the largest outer time step possible is limited not by the physical forces but by resonances between the fast and slow modes. In this paper we show that this problem can be alleviated by using a simple colored noise thermostatting scheme which selectively targets the high frequency modes in the system. For two sample problems, flexible water and solvated alanine dipeptide, we demonstrate that this allows the use of large outer time steps while still obtaining accurate sampling and minimizing the perturbation of the dynamics. Furthermore, this approach is shown to be comparable to constraining fast motions, thus providing an alternative to molecular dynamics with constraints.

accepted for publication by the Journal of Chemical Physics

References in corpus (6)

• Accurate sampling using Langevin dynamics
• Efficient stochastic thermostatting of path integral molecular dynamics
• Nuclear quantum effects in solids using a colored-noise thermostat
• Colored-noise thermostats à la carte
• Coarse Molecular Dynamics of a Peptide Fragment: Free Energy, Kinetics, and Long-Time Dynamics Computations
• Langevin equation with colored noise for constant-temperature molecular dynamics simulations

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