Paper Info
Title
A Histogram-Free Multicanonical Monte Carlo Algorithm for the Basis Expansion of Density of States
Abstract
We report a new multicanonical Monte Carlo (MC) algorithm to obtain the density of states (DOS) for physical systems with continuous state variables in statistical mechanics. Our algorithm is able to obtain an analytical form for the DOS 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 (MUCA) sampling and Wang-Landau (WL) sampling. This is enabled by storing the visited states directly in a data set 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
2017
10.1145/3093172.3093235
PASC
Keywords
DocType
ISSN
Monte Carlo, statistical mechanics, density of states, algorithms
Conference
Proceedings of the Platform for Advanced Scientific Computing Conference (PASC '17). Association for Computing Machinery (ACM), New York, NY, USA, Article 10, 7 pages (2017)
ISBN
Citations 
PageRank 
978-1-4503-5062-4
0
0.34
References 
Authors
5
2
Name
Order
Citations
PageRank
Ying Wai Li121.46
Markus Eisenbach2376.76