Understanding molecular simulation from algorithms to applications 2nd Edition by Daan Frenkel, Berend Smit ISBN 0122673514 978-0122673511
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ISBN 10: 0122673514
ISBN 13: 978-0122673511
Author: Daan Frenkel, Berend Smit
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.
Table of contents:
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
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