Monte Carlo algorithms for the atomistic simulation of condensed polymer phases

Abstract

Significant progress has been made recently in the field of atomistic simulation of polymer melts through the advent of new powerful Monte Carlo methods. This article reviews the state of the art in the area. Sampling the configurational space of a dense polymer system is difficult, because of complications introduced by high density and the connectivity of the chain molecules. We describe how some novel algorithms attempt to solve the problem, compare them using a set of stringent performance criteria and discuss their strengths and their weaknesses, their successes and their failures. Although we have still not reached the stage where realistically long polymeric chains with atomistic detail can be treated successfully, there is ground for hope. Configuration-bias Monte Carlo (CBMC) and its extensions, concerted-rotation (ConRot)-based algorithms, and hybrid Monte Carlo (HMC) have opened up new possibilities for the investigation of more realistic polymer models than the ones used hitherto. The field of possible applications is vast: studies of polymers in melts and in solution, prediction of single-phase thermodynamic properties and phase equilibria, biopolymer modelling and, hopefully, the long-time behaviour of macromolecular systems, may soon become tractable with the rapid evolution of novel Monte Carlo methods.