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Understanding Molecular Simulation
From Algorithms to Applications
- 2nd Edition - October 19, 2001
- Authors: Daan Frenkel, Berend Smit
- Language: English
- Hardback ISBN:9 7 8 - 0 - 1 2 - 2 6 7 3 5 1 - 1
- eBook ISBN:9 7 8 - 0 - 0 8 - 0 5 1 9 9 8 - 2
Understanding Molecular Simulation: From Algorithms to Applications explains the physics behind the "recipes" of molecular simulation for materials science. Computer simulator… Read more
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Request a sales quoteUnderstanding 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.
Graduate students in physics and materials science departments studying molecular simulation techniques; scientists in the fields of polymers, materials science, and applied physics
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
Part V Appendices
A Lagrangian and Hamiltonian
A.1 Lagrangian
A.2 Hamiltonian
A.3 Hamilton Dynamics and Statistical Mechanics
B Non-Hamiltonian Dynamics
B.1 Theoretical Background
B.2 Non-Hamiltonian Simulation of the N, V, T Ensemble
B.3 The N, P, T Ensemble
C Linear Response Theory
C.1 Static Response
C.2 Dynamic Response
C.3 Dissipation
C.4 Elastic Constants
D Statistical Errors
D.1 Static Properties: System Size
D.2 Correlation Functions
D.3 Block Averages
E Integration Schemes
E.1 Higher-Order Schemes
E.2 Nosé-Hoover Algorithms
F Saving CPU Time
F.1 Verlet List
F.2 Cell Lists
F.3 Combining the Verlet and Cell Lists
F.4 Efficiency
G Reference States
G.1 Grand-Canonical Ensemble Simulation
H Statistical Mechanics of the Gibbs Ensemble
H.1 Free Energy of the Gibbs Ensemble
H.2 Chemical Potential in the Gibbs Ensemble
I Overlapping Distribution for Polymers
J Some General Purpose Algorithms
K Small Research Projects
K.1 Adsorption in Porous Media
K.2 Transport Properties in Liquids
K.3 Diffusion in a Porous Media
K.4 Multiple-Time-Step Integrators
K.5 Thermodynamic Integration
L Hints for Programming
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
Part V Appendices
A Lagrangian and Hamiltonian
A.1 Lagrangian
A.2 Hamiltonian
A.3 Hamilton Dynamics and Statistical Mechanics
B Non-Hamiltonian Dynamics
B.1 Theoretical Background
B.2 Non-Hamiltonian Simulation of the N, V, T Ensemble
B.3 The N, P, T Ensemble
C Linear Response Theory
C.1 Static Response
C.2 Dynamic Response
C.3 Dissipation
C.4 Elastic Constants
D Statistical Errors
D.1 Static Properties: System Size
D.2 Correlation Functions
D.3 Block Averages
E Integration Schemes
E.1 Higher-Order Schemes
E.2 Nosé-Hoover Algorithms
F Saving CPU Time
F.1 Verlet List
F.2 Cell Lists
F.3 Combining the Verlet and Cell Lists
F.4 Efficiency
G Reference States
G.1 Grand-Canonical Ensemble Simulation
H Statistical Mechanics of the Gibbs Ensemble
H.1 Free Energy of the Gibbs Ensemble
H.2 Chemical Potential in the Gibbs Ensemble
I Overlapping Distribution for Polymers
J Some General Purpose Algorithms
K Small Research Projects
K.1 Adsorption in Porous Media
K.2 Transport Properties in Liquids
K.3 Diffusion in a Porous Media
K.4 Multiple-Time-Step Integrators
K.5 Thermodynamic Integration
L Hints for Programming
- No. of pages: 664
- Language: English
- Edition: 2
- Published: October 19, 2001
- Imprint: Academic Press
- Hardback ISBN: 9780122673511
- eBook ISBN: 9780080519982
DF
Daan Frenkel
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.
Affiliations and expertise
FOM Institute for Atomic and Molecular Physics, The NetherlandsBS
Berend Smit
Berend Smit is Professor at the Department of Chemical Engineering of the Faculty of Science, University of Amsterdam. His research focuses on novel Monte Carlo simulations. Smit applies this technique to problems that are of technological importance, particularly those of interest in chemical engineering.
Affiliations and expertise
Professor at the Department of Chemical Engineering of the Faculty of Science, University of AmsterdamRead Understanding Molecular Simulation on ScienceDirect