MONTE-CARLO METHODOLOGIES FOR ENHANCED CONFIGURATIONAL SAMPLING OF DENSE SYSTEMS - MOTION OF A SPHERICAL SOLUTE IN A POLYMER MELT AS A MODEL PROBLEM

A number of traditional and novel Monte Carlo (MC) methodologies for c onfigurational sampling in condensed phases are studied. The stochasti c motion of a spherical solute molecule in a melt of short polyethylen e chains is used as a model problem to assess the efficiency of the MC algorithms. Traditional MC methods, such as Metropolis MC and force-b ias MC with or without preferential sampling, are inefficient in impar ting significant mobility to the guest in the dense many-chain system. Two novel MC algorithms, based on local-Hessian information, are intr oduced here for the first time. Multidimensional force- or anti-force- bias along local eigenvector directions, and Metropolis MC with eigenv alue-scaling are found surprisingly inefficient for the problem at han d. Significant mobilities are achieved only with a new energy-biased M C method, which ignores the existing barriers and performs a coarse-gr ained random walk over local energy minima. As well as evaluating the various MC algorithms, this work also addresses questions pertinent to the model problem examined here, namely (i) if polymer segment mobili ty is necessary to obtain significant MC mobility of the solute, and ( ii) what is the onset of solute stochastic diffusion in these systems.