2000/06/30 by Alex Bunker, Burkhard Duenweg, Burkhard Dünweg · 1 citation
Materials Science · Mathematics · Physics and Astronomy · #Bridging (networking) #Computer science #Lattice (music) #Markov Chains and Monte Carlo Methods #Markov chain Monte Carlo #Material Dynamics and Properties #Mathematics #Monte Carlo algorithm #Monte Carlo method #Monte Carlo molecular modeling #Parallel computing #Parallel tempering #Physics #Random walk #Speedup #Statistical physics #Theoretical and Computational Physics #cond-mat.soft
paper · pdf · doi:10.1103/physreve.63.016701
arxiv created 2000/07/03 · openalex publication_date 2000/12/22 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We have developed a technique to accelerate the acquisition of effectively uncorrelated configurations for off-lattice models of dense polymer melts that makes use of both parallel tempering and large-scale Monte Carlo moves. The method is based upon simulating a set of systems in parallel, each of which has a slightly different repulsive core potential, such that a thermodynamic path from full excluded volume to an ideal gas of random walks is generated. While each system is run with standard stochastic dynamics, resulting in an NVT ensemble, we implement the parallel tempering through stochastic swaps between the configurations of adjacent potentials, and the large-scale Monte Carlo moves through attempted pivot and translation moves that reach a realistic acceptance probability as the limit of the ideal gas of random walks is approached. Compared to pure stochastic dynamics, this results in an increased efficiency even for a system of chains as short as N=60 monomers, however at this chain length the large-scale Monte Carlo moves were ineffective. For even longer chains, the speedup becomes substantial, as observed from preliminary data for N=200. We also compare our scheme to the end bridging algorithm of Theodorou et al. For N=60, end bridging must allow a polydispersity of more than 10% in order to relax the end-to-end vector more quickly than our method. The comparison is, however, hampered by the fact that the end-to-end vector becomes a somewhat artificial quantity when one implements end bridging, and is perhaps no longer the slowest dynamic variable.