1998/07/16 by Yukito Iba, George Chikenji, Macoto Kikuchi · 2 citations
Engineering · Materials Science · Physics and Astronomy · #Machine Learning in Materials Science #Phase Equilibria and Thermodynamics #Theoretical and Computational Physics #cond-mat
paper · pdf · doi:10.1143/jpsj.67.3327
published as J. Phys. Soc. Japan, Vol.67, Oct. 1998, 3327-3330 (references added) · 5 Pages, 4 Postscript figures, uses epsf.sty
arxiv created 1998/07/16 · openalex publication_date 1998/10/15 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A novel family of dynamical Monte Carlo algorithms for lattice polymers is proposed. Our central idea is to simulate an extended ensemble in which the self-avoiding condition is systematically weakened. The degree of the self-overlap is controlled in a similar manner as the multicanonical ensemble. As a consequence, the ensemble --the multi-self-overlap ensemble-- contains adequate portions of self-overlapping conformations as well as higher energy ones. It is shown that the multi-self-overlap ensemble algorithm reproduce correctly the canonical averages at finite temperatures of the HP model of lattice proteins. Moreover, it outperforms massively a standard multicanonical algorithm for a difficult example of a polymer with 8-stickers. Alternative algorithm based on exchange Monte Carlo method is also discussed.