https://cea.hal.science/cea-02924046Vernizzi, GrazianoGrazianoVernizziSiena College - Okayama UniversityNguyen, Trung DacTrung DacNguyenOrland, HenriHenriOrlandIPHT - Institut de Physique Théorique - UMR CNRS 3681 - CEA - Commissariat à l'énergie atomique et aux énergies alternatives - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifiquede La Cruz, Monica OlveraMonica Olverade La CruzDepartments of Materials Science and Engineering, Chemistry, Chemical and Biological Engineering, Northwestern UniversitMulticanonical Monte Carlo ensemble growth algorithmHAL CCSD2020[PHYS] Physics [physics]De Laborderie, Emmanuelle2020-08-27 16:30:112023-03-24 14:53:182020-08-27 16:30:11enJournal articles10.1103/PhysRevE.101.0213011We 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.