Optimizing the ensemble for equilibration in broad-histogram Monte Carlo simulations

被引：132

作者：

Trebst, Simon ^{[1
,2
]}

Huse, David A. ^{[3
]}

Troyer, Matthias ^{[1
,2
]}

机构：

[1] Theoretische Physik, Eidgenössische TH Zürich, CH-8093 Zürich, Switzerland

[2] Computational Laboratory, Eidgenössische TH Zürich, CH-8092 Zürich, Switzerland

[3] Department of Physics, Princeton University, Princeton, NJ 08544, United States

来源：

Physical Review E - Statistical, Nonlinear, and Soft Matter Physics | 2004年 / 70卷 / 4 2期

基金：

美国国家科学基金会;

关键词：

Computer simulation - Electronic density of states - Ferromagnetic materials - Free energy - Glass - Large scale systems - Monte Carlo methods - Optimization - Phase equilibria - Phase transitions - Polymers - Statistical mechanics;

D O I：

10.1103/PhysRevE.70.046701

中图分类号：

学科分类号：

摘要：

An adaptive algorithm which optimize the statistical-mechanical ensemble in a generalized broad-histogram Monte Carlo simulation to maximize the system's rate of round trips in total energy was presented. It was found that the scaling of the mean round-trip time from the ground state to the maximum entropy state for this local-update method was O([NInN])2) for both the ferromagnetic and the fully frustrated two-dimensional Ising model with N spins. It was shown that the algorithm substantially outperforms flat-histogram methods such as the Wang-Landau algorithm. This algorithm was widely applicable to study the equilibrium behavior of complex system, such as glasses, dense fluids or polymers.

引用

页码：046701 / 1