Recoil growth: an efficient simulation method for multi-polymer systems

TL;DR

This paper introduces a new Monte Carlo simulation method for multi-polymer systems that uses a biased growth technique with a retractable feeler to efficiently generate long chains, outperforming existing methods at high densities.

Contribution

A novel Monte Carlo scheme employing a retractable feeler for efficient multi-polymer system simulation, extending and improving upon Configurational Bias Monte Carlo.

Findings

Significantly more efficient for long chains and high densities.

High success rates in chain construction and acceptance.

Generalizes the Configurational Bias Monte Carlo method.

Abstract

We present a new Monte Carlo scheme for the efficient simulation of multi-polymer systems. The method permits chains to be inserted into the system using a biased growth technique. The growth proceeds via the use of a retractable feeler, which probes possible pathways ahead of the growing chain. By recoiling from traps and excessively dense regions, the growth process yields high success rates for both chain construction and acceptance. Extensive tests of the method using self-avoiding walks on a cubic lattice show that for long chains and at high densities it is considerably more efficient than Configurational Bias Monte Carlo, of which it may be considered a generalisation.