Monte Carlo Simulations to Study the Effect of Chain Stiffness on Static, Dynamic, and Equation-of-state Properties of Polymer Melts


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"Static and dynamic properties of polymers are affected by the stiffness of the chain molecules. In this work, we investigate static and dynamic properties of a lattice model for semiflexible polymer chains. The model is an extension of Shaffer's bond-fluctuation model and includes attractive interactions between monomers and an adjustable bending energy that determines the Kuhn segment length. For this work, we performed Monte Carlo simulations for polymer melts with a range of values of the bending energy, density, and temperature. We find that the Kuhn segment length increases monotonically with the bending energy for a wide range of bending energies. This allows us to model melts of flexible and semiflexible chains. Results for self diffusion coefficients show that the translational mobility is strongly reduced by increasing chain stiffness. We implemented a bead insertion method and a chain insertion method to calculate the pressure of the melts. While chain insertion is a reliable method to determine the pressure at low filling fractions of the lattice, it becomes very inefficient at higher densities. Bead insertion, on the other hand, yields good statistics, even at high densities, but the evaluation depends on an assumption that breaks down for semiflexible chains. We find that bead and chain insertion give comparable results for the pressure of melts of flexible chains at sufficiently high densities."--Abstract.




Chemical Abstracts


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Physics Briefs


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Simulation Methods for Polymers


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Monte Carlo Simulation in Statistical Physics


Book Description

Monte Carlo Simulation in Statistical Physics deals with the computer simulation of many-body systems in condensed-matter physics and related fields of physics, chemistry and beyond, to traffic flows, stock market fluctuations, etc.). Using random numbers generated by a computer, probability distributions are calculated, allowing the estimation of the thermodynamic properties of various systems. This book describes the theoretical background to several variants of these Monte Carlo methods and gives a systematic presentation from which newcomers can learn to perform such simulations and to analyze their results. This fourth edition has been updated and a new chapter on Monte Carlo simulation of quantum-mechanical problems has been added. To help students in their work a special web server has been installed to host programs and discussion groups (http://wwwcp.tphys.uni-heidelberg.de). Prof. Binder was the winner of the Berni J. Alder CECAM Award for Computational Physics 2001.




A Guide to Monte Carlo Simulations in Statistical Physics


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Dealing with all aspects of Monte Carlo simulation of complex physical systems encountered in condensed-matter physics and statistical mechanics, this book provides an introduction to computer simulations in physics. This edition now contains material describing powerful new algorithms that have appeared since the previous edition was published, and highlights recent technical advances and key applications that these algorithms now make possible. Updates also include several new sections and a chapter on the use of Monte Carlo simulations of biological molecules. Throughout the book there are many applications, examples, recipes, case studies, and exercises to help the reader understand the material. It is ideal for graduate students and researchers, both in academia and industry, who want to learn techniques that have become a third tool of physical science, complementing experiment and analytical theory.










Monte Carlo Methods in Statistical Physics


Book Description

In the seven years since this volume first appeared. there has been an enormous expansion of the range of problems to which Monte Carlo computer simulation methods have been applied. This fact has already led to the addition of a companion volume ("Applications of the Monte Carlo Method in Statistical Physics", Topics in Current Physics. Vol . 36), edited in 1984, to this book. But the field continues to develop further; rapid progress is being made with respect to the implementation of Monte Carlo algorithms, the construction of special-purpose computers dedicated to exe cute Monte Carlo programs, and new methods to analyze the "data" generated by these programs. Brief descriptions of these and other developments, together with numerous addi tional references, are included in a new chapter , "Recent Trends in Monte Carlo Simulations" , which has been written for this second edition. Typographical correc tions have been made and fuller references given where appropriate, but otherwise the layout and contents of the other chapters are left unchanged. Thus this book, together with its companion volume mentioned above, gives a fairly complete and up to-date review of the field. It is hoped that the reduced price of this paperback edition will make it accessible to a wide range of scientists and students in the fields to which it is relevant: theoretical phYSics and physical chemistry , con densed-matter physics and materials science, computational physics and applied mathematics, etc.