Efficient configurational-bias Monte-Carlo simulations of chain molecules with `swarms' of trial configurations

Proposed here is a dynamic Monte-Carlo algorithm that is efficient in simulating dense systems of long flexible chain molecules. It expands on the configurational-bias Monte-Carlo method through the simultaneous generation of a large set of trial configurations. This process is directed by attempting to terminate unfinished chains with a low statistical weight, and replacing these chains with clones (enrichments) of stronger chains. The efficiency of the resulting method is explored by simulating dense polymer brushes. A gain in efficiency of at least three orders of magnitude is observed with respect to the configurational-bias approach, and almost one order of magnitude with respect to recoil-growth Monte-Carlo. Furthermore, the inclusion of `waste recycling' is observed to be a powerful method for extracting meaningful statistics from the discarded configurations.