Off-Lattice Markov Chain Monte Carlo Simulations of Mechanically Driven Polymers

TL;DR

This paper introduces an off-lattice Monte Carlo simulation method for semiflexible polymers under mechanical forces, providing more accurate and unbiased results than traditional on-lattice models.

Contribution

The authors develop a novel off-lattice simulation approach with adaptive non-local moves, improving accuracy in modeling polymer responses to mechanical forces.

Findings

Accurately predicts persistence length and end-to-end distance.

Matches theoretical predictions for polymer stretching.

Eliminates orientational bias present in on-lattice models.

Abstract

We develop off-lattice simulations of semiflexible polymer chains subjected to applied mechanical forces using Markov Chain Monte Carlo. Our approach models the polymer as a chain of fixed-length bonds, with configurations updated through adaptive non-local Monte Carlo moves. This proposed method enables precise calculation of a polymer's response to a wide range of mechanical forces, which traditional on-lattice models cannot achieve. Our approach has shown excellent agreement with theoretical predictions of persistence length and end-to-end distance in quiescent states, as well as stretching distances under tension. Moreover, our model eliminates the orientational bias present in on-lattice models, which significantly impacts calculations such as the scattering function, a crucial technique for revealing polymer conformation.