Parallel-Tempering Monte-Carlo Simulation with Feedback-Optimized Algorithm Applied to a Coil-to-Globule Transition of a Lattice Homopolymer

TL;DR

This paper explores an optimized parallel tempering Monte Carlo method with feedback for simulating a lattice homopolymer's coil-to-globule transition, improving temperature set selection and analyzing chain properties.

Contribution

It introduces a feedback-optimized algorithm for selecting simulation temperatures in parallel tempering, applied to lattice homopolymer simulations of coil-to-globule transitions.

Findings

Optimal number of replicas varies with chain length.

Feedback optimization improves simulation efficiency.

Energy and structural properties are accurately determined.

Abstract

We present a study of the parallel tempering (replica exchange) Monte Carlo method, with special focus on the feedback-optimized parallel tempering algorithm, used for generating an optimal set of simulation temperatures. This method is applied to a lattice simulation of a homopolymer chain undergoing a coil-to-globule transition upon cooling. We select the optimal number of replicas for different chain lengths, N = 25, 50 and 75, using replica's round-trip time in temperature space, in order to determine energy, specific heat, and squared end-to-end distance of the hopolymer chain for the selected temperatures. We also evaluate relative merits of this optimization method.