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Journal Articles Physical Review E Year : 2020

Multicanonical Monte Carlo ensemble growth algorithm

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Abstract

We present a novel 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 re-weighting 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.

Dates and versions

cea-02924046 , version 1 (27-08-2020)

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Graziano Vernizzi, Trung Dac Nguyen, Henri Orland, Monica Olvera de La Cruz. Multicanonical Monte Carlo ensemble growth algorithm. Physical Review E , 2020, 101 (2), ⟨10.1103/PhysRevE.101.021301⟩. ⟨cea-02924046⟩
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