Understanding Molecular Simulation

Understanding Molecular Simulation : From Algorithms to Applications

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Understanding Molecular Simulation: From Algorithms to Applications explains the physics behind the "recipes" of molecular simulation for materials science. Computer simulators are continuously confronted with questions concerning the choice of a particular technique for a given application. A wide variety of tools exist, so the choice of technique requires a good understanding of the basic principles. More importantly, such understanding may greatly improve the efficiency of a simulation program. The implementation of simulation methods is illustrated in pseudocodes and their practical use in the case studies used in the text. Since the first edition only five years ago, the simulation world has changed significantly -- current techniques have matured and new ones have appeared. This new edition deals with these new developments; in particular, there are sections on: Transition path sampling and diffusive barrier crossing to simulaterare events Dissipative particle dynamic as a course-grained simulation technique Novel schemes to compute the long-ranged forces Hamiltonian and non-Hamiltonian dynamics in the context constant-temperature and constant-pressure molecular dynamics simulations Multiple-time step algorithms as an alternative for constraints Defects in solids The pruned-enriched Rosenbluth sampling, recoil-growth, and concerted rotations for complex molecules Parallel tempering for glassy Hamiltonians Examples are included that highlight current applications and the codes of case studies are available on the World Wide Web. Several new examples have been added since the first edition to illustrate recent applications. Questions are included in this new edition. No prior knowledge of computer simulation is assumed.

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Product details

  • Hardback | 664 pages
  • 154 x 230 x 38mm | 997.9g
  • Elsevier Science Publishing Co Inc
  • Academic Press Inc
  • San Diego, United States
  • English
  • Revised
  • 2nd Revised edition
  • 0122673514
  • 9780122673511
  • 578,498

Review quote

".brilliantly maintains a balance between explaining the physical phenomena and performing computations. Its marvelous writing style invites scientists and students to deepen their knowledge of MD simulations."--ComputingReviews.com, January 11, 2013 "... this book brilliantly lays down the scientific foundations of the simulational approach ..."--Prof. Kurt Binder in Physics World, 1997 "... a treasure. The book is a marvellous mix of just enough formalism with an informal and readable style, sufficient detail to understand methodological advances, appropriate mathematics ..."--Prof. Mark A. Ratner in Physics Today, 1997

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About Berend Smit

Daan Frenkel is based at the FOM Institute for Atomic and Molecular Physics and at the Department of Chemistry, University of Amsterdam. His research has three central themes: prediction of phase behavior of complex liquids, modeling the (hydro) dynamics of colloids and microporous structures, and predicting the rate of activated processes. He was awarded the prestigious Spinoza Prize from the Dutch Research Council in 2000. Berend Smit is Professor at the Department of Chemical Engineering of the Faculty of Science, University of Amsterdam. His research focuses on novel Monte Carlo simulations. Smit applies this technique to problems that are of technological importance, particularly those of interest in chemical engineering.

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Table of contents

Preface to the Second Edition Preface List of Symbols 1 Introduction Part I Basics 2 Statistical Mechanics 2.1 Entropy and Temperature 2.2 Classical Statistical Mechanics 2.3 Questions and Exercises 3 Monte Carlo Simulations 3.1 The Monte Carlo Method 3.2 A Basic Monte Carlo Algorithm 3.3 Trial Moves 3.4 Applications 3.5 Questions and Exercises 4 Molecular Dynamics Simulations 4.1 Molecular Dynamics: the Idea 4.2 Molecular Dynamics: a Program 4.3 Equations of Motion 4.4 Computer Experiments 4.5 Some Applications 4.6 Questions and Exercises Part II Ensembles 5 Monte Carlo Simulations in Various Ensembles 5.1 General Approach 5.2 Canonical Ensemble 5.3 Microcanonical Monte Carlo 5.4 Isobaric-Isothermal Ensemble 5.5 Isotension-Isothermal Ensemble 5.6 Grand-Canonical Ensemble 5.7 Questions and Exercises 6 Molecular Dynamics in Various Ensembles 6.1 Molecular Dynamics at Constant Temperature 6.2 Molecular Dynamics at Constant Pressure 6.3 Questions and Exercises Part III Free Energies and Phase Equilibria 7 Free Energy Calculations 7.1 Thermodynamic Integration 7.2 Chemical Potentials 7.3 Other Free Energy Methods 7.4 Umbrella Sampling 7.5 Questions and Exercises 8 The Gibbs Ensemble 8.1 The Gibbs Ensemble Technique 8.2 The Partition Function 8.3 Monte Carlo Simulations 8.4 Applications 8.5 Questions and Exercises 9 Other Methods to Study Coexistence 9.1 Semigrand Ensemble 9.2 Tracing Coexistence Curves 10 Free Energies of Solids 10.1 Thermodynamic Integration 10.2 Free Energies of Solids 10.3 Free Energies of Molecular Solids 10.4 Vacancies and Interstitials 11 Free Energy of Chain Molecules 11.1 Chemical Potential as Reversible Work 11.2 Rosenbluth Sampling Part IV Advanced Techniques 12 Long-Range Interactions 12.1 Ewald Sums 12.2 Fast Multipole Method 12.3 Particle Mesh Approaches 12.4 Ewald Summation in a Slab Geometry 13 Biased Monte Carlo Schemes 13.1 Biased Sampling Techniques 13.2 Chain Molecules 13.3 Generation of Trial Orientations 13.4 Fixed Endpoints 13.5 Beyond Polymers 13.6 Other Ensembles 13.7 Recoil Growth 13.8 Questions and Exercises 14 Accelerating Monte Carlo Sampling 14.1 Parallel Tempering 14.2 Hybrid Monte Carlo 14.3 Cluster Moves 15 Tackling Time-Scale Problems 15.1 Constraints 15.2 On-the-Fly Optimization: Car-Parrinello Approach 15.3 Multiple Time Steps 16 Rare Events 16.1 Theoretical Background 16.2 Bennett-Chandler Approach 16.3 Diffusive Barrier Crossing 16.4 Transition Path Ensemble 16.5 Searching for the Saddle Point 17 Dissipative Particle Dynamics 17.1 Description of the Technique 17.2 Other Coarse-Grained Techniques Part V Appendices A Lagrangian and Hamiltonian A.1 Lagrangian A.2 Hamiltonian A.3 Hamilton Dynamics and Statistical Mechanics B Non-Hamiltonian Dynamics B.1 Theoretical Background B.2 Non-Hamiltonian Simulation of the N, V, T Ensemble B.3 The N, P, T Ensemble C Linear Response Theory C.1 Static Response C.2 Dynamic Response C.3 Dissipation C.4 Elastic Constants D Statistical Errors D.1 Static Properties: System Size D.2 Correlation Functions D.3 Block Averages E Integration Schemes E.1 Higher-Order Schemes E.2 Nosé-Hoover Algorithms F Saving CPU Time F.1 Verlet List F.2 Cell Lists F.3 Combining the Verlet and Cell Lists F.4 Efficiency G Reference States G.1 Grand-Canonical Ensemble Simulation H Statistical Mechanics of the Gibbs Ensemble H.1 Free Energy of the Gibbs Ensemble H.2 Chemical Potential in the Gibbs Ensemble I Overlapping Distribution for Polymers J Some General Purpose Algorithms K Small Research Projects K.1 Adsorption in Porous Media K.2 Transport Properties in Liquids K.3 Diffusion in a Porous Media K.4 Multiple-Time-Step Integrators K.5 Thermodynamic Integration L Hints for Programming Bibliography Author Index Index

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