Paper Info
Title
Histogram-free multicanonical Monte Carlo sampling to calculate the density of states.
Abstract
We report a new multicanonical Monte Carlo algorithm to obtain the density of states for physical systems with continuous state variables in statistical mechanics. Our algorithm is able to obtain a closed-form expression for the density of states expressed in a chosen basis set, instead of a numerical array of finite resolution as in previous variants of this class of MC methods such as the multicanonical sampling and Wang–Landau sampling. This is enabled by storing the visited states directly and avoiding the explicit collection of a histogram. This practice also has the advantage of avoiding undesirable artificial errors caused by the discretization and binning of continuous state variables. Our results show that this scheme is capable of obtaining converged results with a much reduced number of Monte Carlo steps, leading to a significant speedup over existing algorithms.
Year
DOI
Venue
2019
10.1016/j.cpc.2018.09.025
Computer Physics Communications
Keywords
Field
DocType
Monte Carlo,Statistical mechanics,Density of states,Algorithms
Histogram,Discretization,Mathematical optimization,Monte Carlo method,Statistical mechanics,Monte Carlo algorithm,Physical system,Algorithm,State variable,Sampling (statistics),Mathematics
Journal
Volume
ISSN
Citations 
235
0010-4655
0
PageRank 
References 
Authors
0.34
5
3
Name
Order
Citations
PageRank
Alfred C. K. Farris100.34
Ying Wai Li221.46
Markus Eisenbach394.17