Simulation of Lattice Polymers with Multi-Self-Overlap Ensemble

TL;DR

This paper introduces a new Monte Carlo algorithm for lattice polymers that systematically weakens the self-avoiding constraint, enabling efficient sampling of conformations and accurate thermodynamic averages, especially for complex polymer models.

Contribution

The paper proposes the multi-self-overlap ensemble algorithm, a novel Monte Carlo method that improves sampling efficiency for lattice polymers by controlling self-overlap, outperforming standard methods in challenging cases.

Findings

Accurately reproduces canonical averages of lattice protein models.

Outperforms standard multicanonical algorithms in complex polymer examples.

Discusses an alternative exchange Monte Carlo approach.

Abstract

A novel family of dynamical Monte Carlo algorithms for lattice polymers is proposed. Our central idea is to simulate an extended ensemble in which the self-avoiding condition is systematically weakened. The degree of the self-overlap is controlled in a similar manner as the multicanonical ensemble. As a consequence, the ensemble --the multi-self-overlap ensemble-- contains adequate portions of self-overlapping conformations as well as higher energy ones. It is shown that the multi-self-overlap ensemble algorithm reproduce correctly the canonical averages at finite temperatures of the HP model of lattice proteins. Moreover, it outperforms massively a standard multicanonical algorithm for a difficult example of a polymer with 8-stickers. Alternative algorithm based on exchange Monte Carlo method is also discussed.