Efficient configurational-bias Monte-Carlo simulations of chain molecules with “swarms” of trial configurations
(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
- Boon, Niels LU
- organization
- publishing date
- 2018-08-14
- 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
-
- pmid:30111122
- scopus:85051468466
- 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
- 2025-04-04 14:56:37
@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}},
}