2011
DOI: 10.1063/1.3518369
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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 ala… Show more

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Cited by 69 publications
(89 citation statements)
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“…It has been shown that resonance instabilities can be effectively mitigated using specially designed Langevin thermostats. 41,44 The application of these techniques here is beyond the scope of the Luehr, Markland and Martínez -Page 13 present study, but should increase the outer time step by a further factor of 2-4 fold and thus further improve the computational speedups reported here.…”
Section: Resultsmentioning
confidence: 99%
“…It has been shown that resonance instabilities can be effectively mitigated using specially designed Langevin thermostats. 41,44 The application of these techniques here is beyond the scope of the Luehr, Markland and Martínez -Page 13 present study, but should increase the outer time step by a further factor of 2-4 fold and thus further improve the computational speedups reported here.…”
Section: Resultsmentioning
confidence: 99%
“…For GLE simulations we have adopted the method of Colored Noise thermostats [22,23,51]. Since this method can only deal with memory kernels given as a superposition of functions…”
Section: Simulation Detailsmentioning
confidence: 99%
“…Solving the GLE for complex heterogeneous and extended systems is still a challenge. A major step towards the solution of this problem for a realistic application has been recently given [43,44,47,48,52]. In particular, an efficient and transferable algorithm has been developed in Ref.…”
Section: Introductionmentioning
confidence: 99%