Polymer Simulations with a flat histogram stochastic growth algorithm
Thomas Prellberg, Jaroslaw Krawczyk, Andrew Rechnitzer
TL;DR
This paper introduces a new flat histogram Monte Carlo algorithm for polymer lattice models, combining microcanonical reweighting with the PERM method to improve simulation efficiency.
Contribution
The paper presents a novel flat histogram algorithm that enhances polymer simulations by integrating microcanonical reweighting with PERM.
Findings
Efficient sampling of polymer configurations across energy states
Improved accuracy in estimating thermodynamic properties
Demonstrated advantages over traditional methods
Abstract
We present Monte Carlo simulations of lattice models of polymers. These simulations are intended to demonstrate the strengths of a powerful new flat histogram algorithm which is obtained by adding microcanonical reweighting techniques to the pruned and enriched Rosenbluth method (PERM).
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