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Understanding Molecular Simulation explains molecular simulation from a chemical-physics and statistical-mechanics perspective. It highlights how physical concepts are used to develop better algorithms and expand the range of applicability of simulations. Understanding Molecular Simulation is equally relevant for those who develop new code and those who use existing packages. Both groups are continuously confronted with the question of which computational technique best suits a given application. Understanding Molecular Simulation provides readers with the foundational knowledge they need to learn about, select and apply the most appropriate of these tools to their own work. The implementation of simulation methods is illustrated in pseudocodes, and their practical use is shown via case studies presented throughout the text. Since the second edition's publication, the simulation world has expanded significantly: existing techniques have continued to develop, and new ones have emerged, opening up novel application areas. This new edition aims to describe these new developments without becoming exhaustive; examples are included that highlight current uses, and several new examples have been added to illustrate recent applications. Examples, case studies, questions, and downloadable algorithms are also included to support learning. No prior knowledge of computer simulation is assumed.
List of contents
1. Introduction
Part I: Basics
2. Thermodynamics and Statistical Mechanics
3. Monte Carlo Simulations¿
4. Molecular Dynamics Simulations¿
5. Computer Experiments
Part II: Ensembles
6. Monte Carlo Simulations in Various Ensembles¿
7. Molecular Dynamics in Various Ensembles¿
Part III: Free -Energy Calculations
8. Free Energy Calculations
9. Free Energies of Solids¿
10. Free Energy of Chain Molecules¿
Part IV: Advanced Techniques
11. Long-Ranged Interactions
12. Configurational Bias Monte Carlo
13. Accelerating Monte Carlo Sampling¿
14. Time-Scale-separation Problems in MD
15. Rare Events
16. Mesoscopic Fluid Models
Part V: Appendices
A: Lagrangian and Hamiltonian
B: Non-Hamiltonian Dynamics
C: Non-equilibrium Thermodynamics
D: Smoothed Dissipative Particle Dynamics
E: Committor for 1-D diffusive barrier crossing
F: Linear Response Theory: examples
G: Statistical Errors
H: Integration Schemes
I: Saving CPU Time
J: Reference States
K: Statistical Mechanics of the Gibbs "Ensemble"
L: Overlapping Distribution for Polymers
M: Some General Purpose Algorithms
About the author
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.
