Self-adapting fixed-end-point configurational-bias Monte Carlo method for the regrowth of interior segments of chain molecules with strong intramolecular interactions

Collin D. Wick; J. Ilja Siepmann

doi:10.1021/ma000172g

Self-adapting fixed-end-point configurational-bias Monte Carlo method for the regrowth of interior segments of chain molecules with strong intramolecular interactions

Collin D. Wick, J. Ilja Siepmann

Chemistry (Twin Cities)

Research output: Contribution to journal › Article › peer-review

107 Scopus citations

Abstract

The configurational-bias Monte Carlo method is proposed for efficient conformational sampling of the interior segments of chain molecules. An algorithm allows for the simulation of chain molecules using force fields of arbitrary complexity. An initial guess of the biasing probability from a short presimulation or an earlier simulation of a related system or simply use a uniform biasing probability is obtained.

Original language	English (US)
Pages (from-to)	7207-7218
Number of pages	12
Journal	Macromolecules
Volume	33
Issue number	19
DOIs	https://doi.org/10.1021/ma000172g
State	Published - Sep 19 2000

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10.1021/ma000172g

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title = "Self-adapting fixed-end-point configurational-bias Monte Carlo method for the regrowth of interior segments of chain molecules with strong intramolecular interactions",

abstract = "The configurational-bias Monte Carlo method is proposed for efficient conformational sampling of the interior segments of chain molecules. An algorithm allows for the simulation of chain molecules using force fields of arbitrary complexity. An initial guess of the biasing probability from a short presimulation or an earlier simulation of a related system or simply use a uniform biasing probability is obtained.",

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Self-adapting fixed-end-point configurational-bias Monte Carlo method for the regrowth of interior segments of chain molecules with strong intramolecular interactions

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