Online citations, reference lists, and bibliographies.
← Back to Search

Configurational Bias Monte Carlo: A New Sampling Scheme For Flexible Chains

J. I. Siepmann, D. Frenkel
Published 1992 · Physics

Save to my Library
Download PDF
Analyze on Scholarcy
Share
We propose a novel approach that allows efficient numerical simulation of systems consisting of flexible chain molecules. The method is especially suitable for the numerical simulation of dense chain systems and monolayers. A new type of Monte Carlo move is introduced that makes it possible to carry out large scale conformational changes of the chain molecule in a single trial move. Our scheme is based on the selfavoiding random walk algorithm of Rosenbluth and Rosenbluth. As an illustration, we compare the results of a calculation of mean-square end to end lengths for single chains on a two-dimensional square lattice with corresponding data gained from other simulations.
This paper references
10.1063/1.2914118
Scaling Concepts in Polymer Physics
Pierre-Gilles de Gennes (1979)
10.1063/1.457963
Monte Carlo simulation of dense polymer systems on a lattice
S. Geyler (1990)
10.2307/2938686
Computer Simulation of Liquids
M. P. Allen (1988)
10.1063/1.443937
Monte Carlo studies of polymer chain dimensions in the melt
M. Mansfield (1982)
10.1016/0167-7977(88)90015-9
Monte Carlo simulation of lattice models for macromolecules
K. Kremer (1988)
10.1063/1.453758
Computer simulation of long polymers adsorbed on a surface. II. Critical behavior of a single self‐avoiding walk
H. Meirovitch (1988)
10.1080/00268979000101591
A method for the direct calculation of chemical potentials for dense chain systems
J. I. Siepmann (1990)
10.1063/1.1730021
New Method for the Statistical Computation of Polymer Dimensions
F. T. Wall (1959)
10.1080/00268979100102041
Novel scheme to compute chemical potentials of chain molecules on a lattice
G. Mooij (1991)
10.1080/00268978700101491
Direct determination of phase coexistence properties of fluids by Monte Carlo simulation in a new ensemble
A. Panagiotopoulos (1987)
10.1002/MARC.1982.030031202
Simulation of chain arrangement in bulk polymer, 1. Chain dimensions and distribution of the end‐to‐end distance
O. F. Olaj (1982)
10.1063/1.431268
Macromolecular dimensions obtained by an efficient Monte Carlo method without sample attrition
F. T. Wall (1975)
10.1063/1.1741967
MONTE CARLO CALCULATION OF THE AVERAGE EXTENSION OF MOLECULAR CHAINS
M. Rosenbluth (1955)
10.1007/BF01007527
Nonergodicity of local, length-conserving Monte Carlo algorithms for the self-avoiding walk
N. Madras (1987)
10.1063/1.1699114
Equation of state calculations by fast computing machines
N. Metropolis (1953)
10.1016/0009-2614(78)84003-2
On a novel Monte Carlo scheme for simulating water and aqueous solutions
C. Pangali (1978)



This paper is referenced by
10.1073/PNAS.0407950101
Speed-up of Monte Carlo simulations by sampling of rejected states.
D. Frenkel (2004)
10.1016/0009-2614(92)80109-O
Folding of model heteropolymers by configurational-bias Monte Carlo
J. I. Siepmann (1992)
10.1016/J.COLSURFA.2009.09.002
Adsorption of light alkanes and alkenes onto single-walled carbon nanotube bundles: Langmuirian analysis and molecular simulations
F. J. Cruz (2010)
10.1039/B614463M
Modelling studies of water in crystalline nanoporous aluminosilicates.
D. Bougeard (2007)
10.1201/9780367802523-12
Atomic-Scale Simulation of Tribological and Related Phenomena
J. A. Harrison (2020)
10.14288/1.0051665
Particle Markov chain Monte Carlo
Roman Holenstein (2009)
10.1080/14786435.2013.815377
Jamming and crystallization in athermal polymer packings
N. Karayiannis (2013)
10.1016/J.CPLETT.2015.06.024
The effect of unequal strand length on short DNA duplex hybridization in a model microarray system: A Monte Carlo simulation study
Sarah J Cooper (2015)
10.1155/2015/183918
Molecular Dynamics, Monte Carlo Simulations, and Langevin Dynamics: A Computational Review
E. Paquet (2015)
10.1021/ACS.JPCC.6B07493
Screening of Copper Open Metal Site MOFs for Olefin/Paraffin Separations Using DFT-Derived Force Fields
Ambarish Kulkarni (2016)
10.1088/0034-4885/60/5/001
Applications of Monte Carlo methods to statistical physics
K. Binder (1997)
10.7916/D8VM49GH
Effect of Surface Curvature and Chemistry on Protein Stability, Adsorption and Aggregation
M. Radhakrishna (2014)
10.1021/ACS.JPCC.7B03459
Impacts of Gas Impurities from Pipeline Natural Gas on Methane Storage in Metal–Organic Frameworks during Long-Term Cycling
Y. Wu (2017)
10.1016/J.EURPOLYMJ.2004.10.025
Monte Carlo simulation for the adsorption of symmetric triblock copolymers I. Configuration distribution and density profiles of adsorbed chains
J. Li (2005)
10.1063/1.5025726
Predicting vapor liquid equilibria using density functional theory: A case study of argon.
Himanshu Goel (2018)
10.1063/1.2232082
Efficient and precise solvation free energies via alchemical adiabatic molecular dynamics.
J. Abrams (2006)
Advanced Monte Carlo Methods for the Study of Nucleation
T. Loeffler (2016)
10.1007/978-94-011-1956-6_5
MONTE CARLO STUDIES OF THE MICROSCOPIC PROPERTIES OF ORGANIC THIN FILMS
J. I. Siepmann (1993)
Theoretische Untersuchungen zur Adsorption von Arzneistoffen an Mikrokristalliner Celulose
N. Sonnenberg (2008)
10.1103/PHYSREVLETT.100.050602
Dense and nearly jammed random packings of freely jointed chains of tangent hard spheres.
Nikos Ch Karayiannis (2008)
10.1021/CM303131C
Insights into the Topotactic Conversion Process from Layered Silicate RUB-36 to FER-type Zeolite by Layer Reassembly
Zhenchao Zhao (2013)
10.1002/wcms.1414
Chameleon: A generalized, connectivity altering software for tackling properties of realistic polymer systems
Orestis Alexiadis (2019)
10.1134/S1811238213060039
Computer simulation of stiff-chain polymers
V. A. Ivanov (2013)
10.1038/s41467-019-12418-9
Reminiscent capillarity in subnanopores
I. Deroche (2019)
10.1039/c9sm00264b
Predicting the effect of chain-length mismatch on phase separation in noble metal nanoparticle monolayers with chemically mismatched ligands.
Steven N Merz (2019)
10.1021/acs.jctc.6b00973
Assessment and Optimization of Configurational-Bias Monte Carlo Particle Swap Strategies for Simulations of Water in the Gibbs Ensemble.
P. Bai (2017)
10.1380/JSSSJ.21.32
Structures and Properties of Atoms and Molecules Confined in Nanospaces. Dynamic Behavior of Molecules in Nano-Structured Materials as Investigated by Computer Simulation.
K. Mizukami (2000)
10.1039/c5dt03399c
Understanding and solving disorder in the substitution pattern of amino functionalized MIL-47(V).
J. Heinen (2016)
10.2172/1011208
Nuclear Energy Advanced Modeling and Simulation (NEAMS) Waste Integrated Performance and Safety Codes (IPSC): FY10 Development and Integration
L. J. Criscenti (2011)
10.1021/JP076054D
Size Effects on the Solvation of Anions at the Aqueous Liquid−Vapor Interface
Becky L Eggimann (2008)
10.1016/J.CEJ.2008.07.034
Hydrogen sulphide removal from biogas by zeolite adsorption: Part I. GCMC molecular simulations
Paolo Cosoli (2008)
10.1080/08927022.2019.1628228
Corresponding state behaviour of capillary condensation of confined alkanes
S. Singh (2019)
See more
Semantic Scholar Logo Some data provided by SemanticScholar