Cover	1
	Copyright Page	5
	Contents	6
	Preface to the Second Edition	14
	Preface	16
	List of Symbols	20
	Chapter 1. Introduction	24
	Part I: Basics	30
	Chapter 2. Statistical Mechanics	32
	2.1 Entropy and Temperature	32
	2.2 Classical Statistical Mechanics	36
	2.3 Questions and Exercises	40
	Chapter 3. Monte Carlo Simulations	46
	3.1 The Monte Carlo Method	46
	3.2 A Basic Monte Carlo Algorithm	54
	3.3 Trial Moves	66
	3.4 Applications	74
	3.5 Questions and Exercises	81
	Chapter 4. Molecular Dynamics Simulations	86
	4.1 Molecular Dynamics: The Idea	86
	4.2 Molecular Dynamics: A Program	87
	4.3 Equations of Motion	94
	4.4 Computer Experiments	107
	4.5 Some Applications	120
	4.6 Questions and Exercises	128
	Part II: Ensembles	132
	Chapter 5. Monte Carlo Simulations in Various Ensembles	134
	5.1 General Approach	135
	5.2 Canonical Ensemble	135
	5.3 Microcanonical Monte Carlo	137
	5.4 Isobaric-Isothermal Ensemble	138
	5.5 Isotension-Isothermal Ensemble	148
	5.6 Grand-Canonical Ensemble	149
	5.7 Questions and Exercises	158
	Chapter 6. Molecular Dynamics in Various Ensembles	162
	6.1 Molecular Dynamics at Constant Temperature	163
	6.2 Molecular Dynamics at Constant Pressure	181
	6.3 Questions and Exercises	183
	Part III: Free Energies and Phase Equilibria	188
	Chapter 7. Free Energy Calculations	190
	7.1 Thermodynamic Integration	191
	7.2 Chemical Potentials	195
	7.3 Other Free Energy Methods	206
	7.4 Umbrella Sampling	215
	7.5 Questions and Exercises	222
	Chapter 8. The Gibbs Ensemble	224
	8.1 The Gibbs Ensemble Technique	226
	8.2 The Partition Function	227
	8.3 Monte Carlo Simulations	228
	8.4 Applications	243
	8.5 Questions and Exercises	246
	Chapter 9. Other Methods to Study Coexistence	248
	9.1 Semigrand Ensemble	248
	9.2 Tracing Coexistence Curves	256
	Chapter 10. Free Energies of Solids	264
	10.1 Thermodynamic Integration	265
	10.2 Free Energies of Solids	266
	10.3 Free Energies of Molecular Solids	268
	10.4 Vacancies and Interstitials	286
	Chapter 11. Free Energy of Chain Molecules	292
	11.1 Chemical Potential as Reversible Work	292
	11.2 Rosenbluth Sampling	294
	Part IV: Advanced Techniques	312
	Chapter 12. Long-Range Interactions	314
	12.1 Ewald Sums	315
	12.2 Fast Multipole Method	329
	12.3 Particle Mesh Approaches	333
	12.4 Ewald Summation in a Slab Geometry	339
	Chapter 13. Biased Monte Carlo Schemes	344
	13.1 Biased Sampling Techniques	345
	13.2 Chain Molecules	354
	13.3 Generation of Trial Orientations	364
	13.4 Fixed Endpoints	376
	13.5 Beyond Polymers	383
	13.6 Other Ensembles	388
	13.7 Recoil Growth	397
	13.8 Questions and Exercises	406
	Chapter 14. Accelerating Monte Carlo Sampling	412
	14.1 Parallel Tempering	412
	14.2 Hybrid Monte Carlo	420
	14.3 Cluster Moves	422
	Chapter 15. Tackling Time-Scale Problems	432
	15.1 Constraints	433
	15.2 On-the-Fly Optimization: Car-Parrinello Approach	444
	15.3 Multiple Time Steps	447
	Chapter 16. Rare Events	454
	16.1 Theoretical Background	455
	16.2 Bennett-Chandler Approach	459
	16.3 Diffusive Barrier Crossing	466
	16.4 Transition Path Ensemble	473
	16.5 Searching for the Saddle Point	485
	Chapter 17. Dissipative Particle Dynamics	488
	17.1 Description of the Technique	489
	17.2 Other Coarse-Grained Techniques	499
	Part V: Appendices	502
	A Lagrangian and Hamiltonian	504
	A.1 Lagrangian	506
	A.2 Hamiltonian	509
	A.3 Hamilton Dynamics and Statistical Mechanics	511
	B Non-Hamiltonian Dynamics	518
	B.1 Theoretical Background	518
	B.2 Non-Hamiltonian Simulation of the N,V,T Ensemble	520
	B.3 The N,P,T Ensemble	528
	C Linear Response Theory	532
	C.1 Static Response	532
	C.2 Dynamic Response	534
	C.3 Dissipation	536
	C.4 Elastic Constants	542
	D Statistical Errors	548
	D.1 Static Properties: System Size	548
	D.2 Correlation Functions	550
	D.3 Block Averages	552
	E Integration Schemes	556
	E.1 Higher-Order Schemes	556
	E.2 Nosé-Hoover Algorithms	558
	F Saving CPU Time	568
	F.1 Verlet List	568
	F.2 Cell Lists	573
	F.3 Combining the Verlet and Cell Lists	573
	F.4 Efficiency	575
	G Reference States	582
	G.1 Grand-Canonical Ensemble Simulation	582
	H Statistical Mechanics of the Gibbs Ensemble	586
	H.1 Free Energy of the Gibbs Ensemble	586
	H.2 Chemical Potential in the Gibbs Ensemble	593
	I Overlapping Distribution for Polymers	596
	J Some General Purpose Algorithms	600
	K Small Research Projects	604
	K.1 Adsorption in Porous Media	604
	K.2 Transport Properties in Liquids	605
	K.3 Diffusion in a Porous Media	606
	K.4 Multiple-Time-Step Integrators	607
	K.5 Thermodynamic Integration	608
	L Hints for Programming	610
	Bibliography	612
	Author Index	642
	Index	651