A constrained maximum entropy method in polymer statistics

Abstract

A modified version of the maximum entropy principle, called “constrained maximum entropy” method (MEC), is revisited to combine the information obtained in computer simulations of polymers with external information in the form of configurational averages. A random-temperature molecular dynamics trajectory is being proposed as a biased random walk in configurational space to be reweighted by using the given average information. This random walk, generating a “meta” configurational probability, has been found to contain relevant information on the system. The method is compared with other computational techniques, like the generalized-ensemble and configurational-biased Monte Carlo, for simple models in the field of polymers and biopolymers. The main features of polymer configurational distribution functions of interest in polymer physics are consistent among the different methods in a wide range of temperatures and especially at room conditions. The advantage of the MEC approach is in taking into account all the degrees of freedom in the model, thus allowing applications in complicated biopolymers in the explicit solvent.