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Journal ArticleDOI

Monte Carlo of Chains with Excluded Volume: a Way to Evade Sample Attrition

Z. Alexandrowicz
- 15 Jul 1969 - 
- Vol. 51, Iss: 2, pp 561-565
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TLDR
In this paper, a new method for the generation of long self-avoiding walks on lattice is described, where short self avoiding walks are generated by direct Monte Carlo method, then linked in pairs to form "dimers".
Abstract
A new method for the generation of long self‐avoiding walks on lattice is described: Short self‐avoiding walks—say of length N = 50—are generated by direct Monte Carlo method, then linked in pairs to form “dimers.” Each dimer is tested for intersections between its two halves—those passing the test giving a sample of self‐avoiding walks N = 100, which is dimerized in turn, etc. In this manner the repeated checking for intersections formed with only a few steps (short loops) is substantially avoided, while precisely such intersections are responsible for the heavy attrition with the direct Monte Carlo method. Thus the attrition accompanying the dimerization is quite insignificant even for very large N. With the help of this method walks N = 50 × 27 = 6400 were generated on the 4‐choice cubic lattice, for which the expansion coefficient of the end to end distance is α = 2.2. (The limit reached by others is N ≃ 2000 on the tetrahedral lattice, corresponding to only α ≃ 1.6, while α = 2.2 would require a leng...

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Citations
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Journal ArticleDOI

The pivot algorithm: A highly efficient Monte Carlo method for the self-avoiding walk

TL;DR: This paper finds that the pivot algorithm is extraordinarily efficient: one “effectively independent” sample can be produced in a computer time of orderN, and presents a rigorous proof of ergodicity and numerical results on self-avoiding walks in two and three dimensions.
Journal ArticleDOI

Monte Carlo simulation of lattice models for macromolecules

TL;DR: This article reviews various methods for the Monte Carlo simulation of models for long flexible polymer chains, namely self-avoiding random walks at various lattices, and discusses the basic ideas on which the various algorithms are based as well as their limitations.
Journal ArticleDOI

Macromolecular dimensions obtained by an efficient Monte Carlo method without sample attrition

TL;DR: In this paper, the statistical dimensions of macromolecular chains of fixed contour length can be rapidly calculated by Monte Carlo methods applied to a model consisting of dynamic self-avoiding random chains on a lattice.
Journal ArticleDOI

Geometrical cluster growth models and kinetic gelation

Hans J. Herrmann
- 01 Apr 1986 - 
TL;DR: The paper begins with an introduction to the field of growth models showing what makes them different from stalic models and a principal role in these relations plays the fractal dimension.
Book ChapterDOI

DNA bending, flexibility, and helical repeat by cyclization kinetics.

TL;DR: Experimental approaches to the measurement of ring closure probability and the theory used to analyze ring closure, and thereby obtain fundamental physical information from the J factor are discussed.
References
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Journal ArticleDOI

The Configuration of Real Polymer Chains

TL;DR: In this article, the average linear dimension of a polymer chain is taken proportional to a power of the chain length, that power must be greater than the value 0.50 previously deduced in the conventional ''random flight'' treatment of molecular configuration.
Journal ArticleDOI

New Method for the Statistical Computation of Polymer Dimensions

TL;DR: In this article, a new method was described for the generation of excluded volume random walks of contour lengths comparable to those of real polymer molecules using a high speed electronic digital computer.
Journal ArticleDOI

Radius of Gyration of Polymer Chains. II. Segment Density and Excluded Volume Effects

TL;DR: In this paper, the segment density ρ(r) at a distance r from the center of mass is evaluated for chains constrained to have a large specified radius of gyration R, and it is shown that ρ (r)≅(N/2π2)[r2(2R2−r2)12]−1H(2r2− r2)2], where N is the number of segments and H( ) is the step function.
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