Multicanonical Monte Carlo ensemble growth algorithm

Graziano Vernizzi*, Trung Dac Nguyen, Henri Orland, Monica Olvera De La Cruz

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

We present an ensemble Monte Carlo growth method to sample the equilibrium thermodynamic properties of random chains. The method is based on the multicanonical technique of computing the density of states in the energy space. Such a quantity is temperature independent, and therefore microcanonical and canonical thermodynamic quantities, including the free energy, entropy, and thermal averages, can be obtained by reweighting with a Boltzmann factor. The algorithm we present combines two approaches: The first is the Monte Carlo ensemble growth method, where a "population" of samples in the state space is considered, as opposed to traditional sampling by long random walks, or iterative single-chain growth. The second is the flat-histogram Monte Carlo, similar to the popular Wang-Landau sampling, or to multicanonical chain-growth sampling. We discuss the performance and relative simplicity of the proposed algorithm, and we apply it to known test cases.

Original languageEnglish (US)
Article number021301
JournalPhysical Review E
Volume101
Issue number2
DOIs
StatePublished - Feb 2020

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Condensed Matter Physics

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