We have developed a technique to accelerate the acquisition of effectively
uncorrelated configurations for off-lattice models of dense polymer melts t
hat 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 thermo
dynamic path from full excluded volume to an ideal gas of random walks is g
enerated. While each system is run with standard stochastic dynamics, resul
ting in an NVT ensemble, we implement the parallel tempering through stocha
stic 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 resu
lts 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-e
nd vector more quickly than our method. The comparison is, however, hampere
d by the fact that the end-to-end vector becomes a somewhat artificial quan
tity when one implements end bridging, and is perhaps no longer the slowest
dynamic variable.