Understanding Molecular Simulation: From Algorithms to Applications
Frenkel, Daan; Smit, Berend
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Table of contents
- Copyright Pageiv
- Contentsv
- Preface to the Second Editionxiii
- Prefacexv
- List of Symbolsxix
- Chapter 1. Introduction1
- Part I: Basics7
- Chapter 2. Statistical Mechanics9
- 2.1 Entropy and Temperature9
- 2.2 Classical Statistical Mechanics13
- 2.3 Questions and Exercises17
- Chapter 3. Monte Carlo Simulations23
- 3.1 The Monte Carlo Method23
- 3.2 A Basic Monte Carlo Algorithm31
- 3.3 Trial Moves43
- 3.4 Applications51
- 3.5 Questions and Exercises58
- Chapter 4. Molecular Dynamics Simulations63
- 4.1 Molecular Dynamics: The Idea63
- 4.2 Molecular Dynamics: A Program64
- 4.3 Equations of Motion71
- 4.4 Computer Experiments84
- 4.5 Some Applications97
- 4.6 Questions and Exercises105
- Part II: Ensembles109
- Chapter 5. Monte Carlo Simulations in Various Ensembles111
- 5.1 General Approach112
- 5.2 Canonical Ensemble112
- 5.3 Microcanonical Monte Carlo114
- 5.4 Isobaric-Isothermal Ensemble115
- 5.5 Isotension-Isothermal Ensemble125
- 5.6 Grand-Canonical Ensemble126
- 5.7 Questions and Exercises135
- Chapter 6. Molecular Dynamics in Various Ensembles139
- 6.1 Molecular Dynamics at Constant Temperature140
- 6.2 Molecular Dynamics at Constant Pressure158
- 6.3 Questions and Exercises160
- Part III: Free Energies and Phase Equilibria165
- Chapter 7. Free Energy Calculations167
- 7.1 Thermodynamic Integration168
- 7.2 Chemical Potentials172
- 7.3 Other Free Energy Methods183
- 7.4 Umbrella Sampling192
- 7.5 Questions and Exercises199
- Chapter 8. The Gibbs Ensemble201
- 8.1 The Gibbs Ensemble Technique203
- 8.2 The Partition Function204
- 8.3 Monte Carlo Simulations205
- 8.4 Applications220
- 8.5 Questions and Exercises223
- Chapter 9. Other Methods to Study Coexistence225
- 9.1 Semigrand Ensemble225
- 9.2 Tracing Coexistence Curves233
- Chapter 10. Free Energies of Solids241
- 10.1 Thermodynamic Integration242
- 10.2 Free Energies of Solids243
- 10.3 Free Energies of Molecular Solids245
- 10.4 Vacancies and Interstitials263
- Chapter 11. Free Energy of Chain Molecules269
- 11.1 Chemical Potential as Reversible Work269
- 11.2 Rosenbluth Sampling271
- Part IV: Advanced Techniques289
- Chapter 12. Long-Range Interactions291
- 12.1 Ewald Sums292
- 12.2 Fast Multipole Method306
- 12.3 Particle Mesh Approaches310
- 12.4 Ewald Summation in a Slab Geometry316
- Chapter 13. Biased Monte Carlo Schemes321
- 13.1 Biased Sampling Techniques322
- 13.2 Chain Molecules331
- 13.3 Generation of Trial Orientations341
- 13.4 Fixed Endpoints353
- 13.5 Beyond Polymers360
- 13.6 Other Ensembles365
- 13.7 Recoil Growth374
- 13.8 Questions and Exercises383
- Chapter 14. Accelerating Monte Carlo Sampling389
- 14.1 Parallel Tempering389
- 14.2 Hybrid Monte Carlo397
- 14.3 Cluster Moves399
- Chapter 15. Tackling Time-Scale Problems409
- 15.1 Constraints410
- 15.2 On-the-Fly Optimization: Car-Parrinello Approach421
- 15.3 Multiple Time Steps424
- Chapter 16. Rare Events431
- 16.1 Theoretical Background432
- 16.2 Bennett-Chandler Approach436
- 16.3 Diffusive Barrier Crossing443
- 16.4 Transition Path Ensemble450
- 16.5 Searching for the Saddle Point462
- Chapter 17. Dissipative Particle Dynamics465
- 17.1 Description of the Technique466
- 17.2 Other Coarse-Grained Techniques476
- Part V: Appendices479
- A Lagrangian and Hamiltonian481
- A.1 Lagrangian483
- A.2 Hamiltonian486
- A.3 Hamilton Dynamics and Statistical Mechanics488
- B Non-Hamiltonian Dynamics495
- B.1 Theoretical Background495
- B.2 Non-Hamiltonian Simulation of the N,V,T Ensemble497
- B.3 The N,P,T Ensemble505
- C Linear Response Theory509
- C.1 Static Response509
- C.2 Dynamic Response511
- C.3 Dissipation513
- C.4 Elastic Constants519
- D Statistical Errors525
- D.1 Static Properties: System Size525
- D.2 Correlation Functions527
- D.3 Block Averages529
- E Integration Schemes533
- E.1 Higher-Order Schemes533
- E.2 Nosé-Hoover Algorithms535
- F Saving CPU Time545
- F.1 Verlet List545
- F.2 Cell Lists550
- F.3 Combining the Verlet and Cell Lists550
- F.4 Efficiency552
- G Reference States559
- G.1 Grand-Canonical Ensemble Simulation559
- H Statistical Mechanics of the Gibbs Ensemble563
- H.1 Free Energy of the Gibbs Ensemble563
- H.2 Chemical Potential in the Gibbs Ensemble570
- I Overlapping Distribution for Polymers573
- J Some General Purpose Algorithms577
- K Small Research Projects581
- K.1 Adsorption in Porous Media581
- K.2 Transport Properties in Liquids582
- K.3 Diffusion in a Porous Media583
- K.4 Multiple-Time-Step Integrators584
- K.5 Thermodynamic Integration585
- L Hints for Programming587
- Bibliography589
- Author Index619
- Index628
Book details
- Vendor Elsevier S & T
- SKU 9780122673511R150
- ISBN-13 9780080519982
- Author Frenkel, Daan; Smit, Berend
- Edition 2nd
- Category Technology & Engineering
- Subject Chemical & Biochemical
Do you have questions about this book?
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.
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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