| Preface to the Second Edition | xiii |
| Preface | xv |
| List of Symbols | xix |
| 1 | Introduction | 1 |
| Part I | Basics | 7 |
| 2 | Statistical Mechanics | 9 |
| 2.1 | Entropy and Temperature | 9 |
| 2.2 | Classical Statistical Mechanics | 13 |
| 2.2.1 | Ergodicity | 15 |
| 2.3 | Questions and Exercises | 17 |
| 3 | Monte Carlo Simulations | 23 |
| 3.1 | The Monte Carlo Method | 23 |
| 3.1.1 | Importance Sampling | 24 |
| 3.1.2 | The Metropolis Method | 27 |
| 3.2 | A Basic Monte Carlo Algorithm | 31 |
| 3.2.1 | The Algorithm | 31 |
| 3.2.2 | Technical Details | 32 |
| 3.2.3 | Detailed Balance versus Balance | 42 |
| 3.3 | Trial Moves | 43 |
| 3.3.1 | Translational Moves | 43 |
| 3.3.2 | Orientational Moves | 48 |
| 3.4 | Applications | 51 |
| 3.5 | Questions and Exercises | 58 |
| 4 | Molecular Dynamics Simulations | 63 |
| 4.1 | Molecular Dynamics: The Idea | 63 |
| 4.2 | Molecular Dynamics: A Program | 64 |
| 4.2.1 | Initialization | 65 |
| 4.2.2 | The Force Calculation | 67 |
| 4.2.3 | Integrating the Equations of Motion | 69 |
| 4.3 | Equations of Motion | 71 |
| 4.3.1 | Other Algorithms | 74 |
| 4.3.2 | Higher-Order Schemes | 77 |
| 4.3.3 | Liouville Formulation of Time-Reversible Algorithms | 77 |
| 4.3.4 | Lyapunov Instability | 81 |
| 4.3.5 | One More Way to Look at the Verlet Algorithm | 82 |
| 4.4 | Computer Experiments | 84 |
| 4.4.1 | Diffusion | 87 |
| 4.4.2 | Order-n Algorithm to Measure Correlations | 90 |
| 4.5 | Some Applications | 97 |
| 4.6 | Questions and Exercises | 105 |
| Part II | Ensembles | 109 |
| 5 | Monte Carlo Simulations in Various Ensembles | 111 |
| 5.1 | General Approach | 112 |
| 5.2 | Canonical Ensemble | 112 |
| 5.2.1 | Monte Carlo Simulations | 113 |
| 5.2.2 | Justification of the Algorithm | 114 |
| 5.3 | Microcanonical Monte Carlo | 114 |
| 5.4 | Isobaric-Isothermal Ensemble | 115 |
| 5.4.1 | Statistical Mechanical Basis | 116 |
| 5.4.2 | Monte Carlo Simulations | 119 |
| 5.4.3 | Applications | 122 |
| 5.5 | Isotension-Isothermal Ensemble | 125 |
| 5.6 | Grand-Canonical Ensemble | 126 |
| 5.6.1 | Statistical Mechanical Basis | 127 |
| 5.6.2 | Monte Carlo Simulations | 130 |
| 5.6.3 | Justification of the Algorithm | 130 |
| 5.6.4 | Applications | 133 |
| 5.7 | Questions and Exercises | 135 |
| 6 | Molecular Dynamics in Various Ensembles | 139 |
| 6.1 | Molecular Dynamics at Constant Temperature | 140 |
| 6.1.1 | The Andersen Thermostat | 141 |
| 6.1.2 | Nose-Hoover Thermostat | 147 |
| 6.1.3 | Nose-Hoover Chains | 155 |
| 6.2 | Molecular Dynamics at Constant Pressure | 158 |
| 6.3 | Questions and Exercises | 160 |
| Part III | Free Energies and Phase Equilibria | 165 |
| 7 | Free Energy Calculations | 167 |
| 7.1 | Thermodynamic Integration | 168 |
| 7.2 | Chemical Potentials | 172 |
| 7.2.1 | The Particle Insertion Method | 173 |
| 7.2.2 | Other Ensembles | 176 |
| 7.2.3 | Overlapping Distribution Method | 179 |
| 7.3 | Other Free Energy Methods | 183 |
| 7.3.1 | Multiple Histograms | 183 |
| 7.3.2 | Acceptance Ratio Method | 189 |
| 7.4 | Umbrella Sampling | 192 |
| 7.4.1 | Nonequilibrium Free Energy Methods | 196 |
| 7.5 | Questions and Exercises | 199 |
| 8 | The Gibbs Ensemble | 201 |
| 8.1 | The Gibbs Ensemble Technique | 203 |
| 8.2 | The Partition Function | 204 |
| 8.3 | Monte Carlo Simulations | 205 |
| 8.3.1 | Particle Displacement | 205 |
| 8.3.2 | Volume Change | 206 |
| 8.3.3 | Particle Exchange | 208 |
| 8.3.4 | Implementation | 208 |
| 8.3.5 | Analyzing the Results | 214 |
| 8.4 | Applications | 220 |
| 8.5 | Questions and Exercises | 223 |
| 9 | Other Methods to Study Coexistence | 225 |
| 9.1 | Semigrand Ensemble | 225 |
| 9.2 | Tracing Coexistence Curves | 233 |
| 10 | Free Energies of Solids | 241 |
| 10.1 | Thermodynamic Integration | 242 |
| 10.2 | Free Energies of Solids | 243 |
| 10.2.1 | Atomic Solids with Continuous Potentials | 244 |
| 10.3 | Free Energies of Molecular Solids | 245 |
| 10.3.1 | Atomic Solids with Discontinuous Potentials | 248 |
| 10.3.2 | General Implementation Issues | 249 |
| 10.4 | Vacancies and Interstitials | 263 |
| 10.4.1 | Free Energies | 263 |
| 10.4.2 | Numerical Calculations | 266 |
| 11 | Free Energy of Chain Molecules | 269 |
| 11.1 | Chemical Potential as Reversible Work | 269 |
| 11.2 | Rosenbluth Sampling | 271 |
| 11.2.1 | Macromolecules with Discrete Conformations | 271 |
| 11.2.2 | Extension to Continuously Deformable Molecules | 276 |
| 11.2.3 | Overlapping Distribution Rosenbluth Method | 282 |
| 11.2.4 | Recursive Sampling | 283 |
| 11.2.5 | Pruned-Enriched Rosenbluth Method | 285 |
| Part IV | Advanced Techniques | 289 |
| 12 | Long-Range Interactions | 291 |
| 12.1 | Ewald Sums | 292 |
| 12.1.1 | Point Charges | 292 |
| 12.1.2 | Dipolar Particles | 300 |
| 12.1.3 | Dielectric Constant | 301 |
| 12.1.4 | Boundary Conditions | 303 |
| 12.1.5 | Accuracy and Computational Complexity | 304 |
| 12.2 | Fast Multipole Method | 306 |
| 12.3 | Particle Mesh Approaches | 310 |
| 12.4 | Ewald Summation in a Slab Geometry | 316 |
| 13 | Biased Monte Carlo Schemes | 321 |
| 13.1 | Biased Sampling Techniques | 322 |
| 13.1.1 | Beyond Metropolis | 323 |
| 13.1.2 | Orientational Bias | 323 |
| 13.2 | Chain Molecules | 331 |
| 13.2.1 | Configurational-Bias Monte Carlo | 331 |
| 13.2.2 | Lattice Models | 332 |
| 13.2.3 | Off-lattice Case | 336 |
| 13.3 | Generation of Trial Orientations | 341 |
| 13.3.1 | Strong Intramolecular Interactions | 342 |
| 13.3.2 | Generation of Branched Molecules | 350 |
| 13.4 | Fixed Endpoints | 353 |
| 13.4.1 | Lattice Models | 353 |
| 13.4.2 | Fully Flexible Chain | 355 |
| 13.4.3 | Strong Intramolecular Interactions | 357 |
| 13.4.4 | Rebridging Monte Carlo | 357 |
| 13.5 | Beyond Polymers | 360 |
| 13.6 | Other Ensembles | 365 |
| 13.6.1 | Grand-Canonical Ensemble | 365 |
| 13.6.2 | Gibbs Ensemble Simulations | 370 |
| 13.7 | Recoil Growth | 374 |
| 13.7.1 | Algorithm | 376 |
| 13.7.2 | Justification of the Method | 379 |
| 13.8 | Questions and Exercises | 383 |
| 14 | Accelerating Monte Carlo Sampling | 389 |
| 14.1 | Parallel Tempering | 389 |
| 14.2 | Hybrid Monte Carlo | 397 |
| 14.3 | Cluster Moves | 399 |
| 14.3.1 | Clusters | 399 |
| 14.3.2 | Early Rejection Scheme | 405 |
| 15 | Tackling Time-Scale Problems | 409 |
| 15.1 | Constraints | 410 |
| 15.1.1 | Constrained and Unconstrained Averages | 415 |
| 15.2 | On-the-Fly Optimization: Car-Parrinello Approach | 421 |
| 15.3 | Multiple Time Steps | 424 |
| 16 | Rare Events | 431 |
| 16.1 | Theoretical Background | 432 |
| 16.2 | Bennett-Chandler Approach | 436 |
| 16.2.1 | Computational Aspects | 438 |
| 16.3 | Diffusive Barrier Crossing | 443 |
| 16.4 | Transition Path Ensemble | 450 |
| 16.4.1 | Path Ensemble | 451 |
| 16.4.2 | Monte Carlo Simulations | 454 |
| 16.5 | Searching for the Saddle Point | 462 |
| 17 | Dissipative Particle Dynamics | 465 |
| 17.1 | Description of the Technique | 466 |
| 17.1.1 | Justification of the Method | 467 |
| 17.1.2 | Implementation of the Method | 469 |
| 17.1.3 | DPD and Energy Conservation | 473 |
| 17.2 | Other Coarse-Grained Techniques | 476 |
| Part V | Appendices | 479 |
| A | Lagrangian and Hamiltonian | 481 |
| A.1 | Lagrangian | 483 |
| A.2 | Hamiltonian | 486 |
| A.3 | Hamilton Dynamics and Statistical Mechanics | 488 |
| A.3.1 | Canonical Transformation | 489 |
| A.3.2 | Symplectic Condition | 490 |
| A.3.3 | Statistical Mechanics | 492 |
| B | Non-Hamiltonian Dynamics | 495 |
| B.1 | Theoretical Background | 495 |
| B.2 | Non-Hamiltonian Simulation of the N, V, T Ensemble | 497 |
| B.2.1 | The Nose-Hoover Algorithm | 498 |
| B.2.2 | Nose-Hoover Chains | 502 |
| B.3 | The N, P, T Ensemble | 505 |
| C | Linear Response Theory | 509 |
| C.1 | Static Response | 509 |
| C.2 | Dynamic Response | 511 |
| C.3 | Dissipation | 513 |
| C.3.1 | Electrical Conductivity | 516 |
| C.3.2 | Viscosity | 518 |
| C.4 | Elastic Constants | 519 |
| D | Statistical Errors | 525 |
| D.1 | Static Properties: System Size | 525 |
| D.2 | Correlation Functions | 527 |
| D.3 | Block Averages | 529 |
| E | Integration Schemes | 533 |
| E.1 | Higher-Order Schemes | 533 |
| E.2 | Nose-Hoover Algorithms | 535 |
| E.2.1 | Canonical Ensemble | 536 |
| E.2.2 | The Isothermal-Isobaric Ensemble | 540 |
| F | Saving CPU Time | 545 |
| F.1 | Verlet List | 545 |
| F.2 | Cell Lists | 550 |
| F.3 | Combining the Verlet and Cell Lists | 550 |
| F.4 | Efficiency | 552 |
| G | Reference States | 559 |
| G.1 | Grand-Canonical Ensemble Simulation | 559 |
| H | Statistical Mechanics of the Gibbs "Ensemble" | 563 |
| H.1 | Free Energy of the Gibbs Ensemble | 563 |
| H.1.1 | Basic Definitions | 563 |
| H.1.2 | Free Energy Density | 565 |
| H.2 | Chemical Potential in the Gibbs Ensemble | 570 |
| I | Overlapping Distribution for Polymers | 573 |
| J | Some General Purpose Algorithms | 577 |
| K | Small Research Projects | 581 |
| K.1 | Adsorption in Porous Media | 581 |
| K.2 | Transport Properties in Liquids | 582 |
| K.3 | Diffusion in a Porous Media | 583 |
| K.4 | Multiple-Time-Step Integrators | 584 |
| K.5 | Thermodynamic Integration | 585 |
| L | Hints for Programming | 587 |
| Bibliography | 589 |
| Author Index | 619 |
| Index | 628 |