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Efficient configurational-bias Monte-Carlo simulations of chain molecules with “swarms” of trial configurations

Boon, Niels LU (2018) In Journal of Chemical Physics 149(6).
Abstract

The pruned-enriched Rosenbluth method (PERM) is a popular and powerful Monte-Carlo technique for sampling flexible chain polymers of substantial length. In its original form, however, the method cannot be applied in Markov-chain Monte-Carlo schemes, which has rendered PERM unsuited for systems that consist of many chains. The current work builds on the configurational-bias Monte-Carlo (CBMC) method. The growth of a large set of trial configurations in each move is governed by simultaneous pruning and enrichment events, which tend to replace configurations with a low statistical weight by clones of stronger configurations. In simulations of dense brushes of flexible chains, a gain in efficiency of at least three orders of magnitude is... (More)

The pruned-enriched Rosenbluth method (PERM) is a popular and powerful Monte-Carlo technique for sampling flexible chain polymers of substantial length. In its original form, however, the method cannot be applied in Markov-chain Monte-Carlo schemes, which has rendered PERM unsuited for systems that consist of many chains. The current work builds on the configurational-bias Monte-Carlo (CBMC) method. The growth of a large set of trial configurations in each move is governed by simultaneous pruning and enrichment events, which tend to replace configurations with a low statistical weight by clones of stronger configurations. In simulations of dense brushes of flexible chains, a gain in efficiency of at least three orders of magnitude is observed with respect to CBMC and one order of magnitude with respect to recoil-growth approaches. Moreover, meaningful statistics can be collected from all trial configurations through the so-called “waste-recycling” Monte Carlo scheme.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Journal of Chemical Physics
volume
149
issue
6
article number
064109
publisher
American Institute of Physics (AIP)
external identifiers
  • scopus:85051468466
  • pmid:30111122
ISSN
0021-9606
DOI
10.1063/1.5029566
language
English
LU publication?
yes
id
2d771ad0-d08b-4980-8a14-66f942751159
date added to LUP
2018-09-10 10:03:22
date last changed
2021-09-15 04:47:28
@article{2d771ad0-d08b-4980-8a14-66f942751159,
  abstract     = {<p>The pruned-enriched Rosenbluth method (PERM) is a popular and powerful Monte-Carlo technique for sampling flexible chain polymers of substantial length. In its original form, however, the method cannot be applied in Markov-chain Monte-Carlo schemes, which has rendered PERM unsuited for systems that consist of many chains. The current work builds on the configurational-bias Monte-Carlo (CBMC) method. The growth of a large set of trial configurations in each move is governed by simultaneous pruning and enrichment events, which tend to replace configurations with a low statistical weight by clones of stronger configurations. In simulations of dense brushes of flexible chains, a gain in efficiency of at least three orders of magnitude is observed with respect to CBMC and one order of magnitude with respect to recoil-growth approaches. Moreover, meaningful statistics can be collected from all trial configurations through the so-called “waste-recycling” Monte Carlo scheme.</p>},
  author       = {Boon, Niels},
  issn         = {0021-9606},
  language     = {eng},
  month        = {08},
  number       = {6},
  publisher    = {American Institute of Physics (AIP)},
  series       = {Journal of Chemical Physics},
  title        = {Efficient configurational-bias Monte-Carlo simulations of chain molecules with “swarms” of trial configurations},
  url          = {http://dx.doi.org/10.1063/1.5029566},
  doi          = {10.1063/1.5029566},
  volume       = {149},
  year         = {2018},
}