描述
开 本: 16开纸 张: 胶版纸包 装: 平装-胶订是否套装: 否国际标准书号ISBN: 9787523218570
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【理论实践双擎驱动】
由玻尔兹曼奖得主达恩弗伦克尔与计算化学巨擘贝伦德·斯密特联合撰写，凝聚两位泰斗数十年研究精髓，将分子模拟的物理学原理与工业级算法无缝衔接，是领域内不可逾越的里程碑式著作。 -
【算法–案例–代码三维一体，实战能力跃升】
特色“伪代码 案例研究 FORTRAN源码”教学模式：伪代码直击算法核心逻辑，案例拆解仿真陷阱与优化策略，配套代码实现即学即用，攻克从理论到编程的最后一公里。 -
【多层级适配，科研与工程通用指南】
既详解恒温恒压模拟、缺陷分析等基础原理，又剖析剪枝富集采样、协同旋转等复杂技术，适合计算化学、材料科学领域的研究生、工程师及跨学科研究者阶梯式精进。
由达恩·弗伦克尔和贝伦德·斯密特所著的《分子模拟：从算法到应用》解释了材料科学分子模拟背后的物理学。由于计算机模拟人员一直面临着为特定应用选择特定技术的问题，并且现有的工具种类繁多，所以在选择技术时需要充分了解基本原理。更重要的是，这种对基本原理的理解可以大大提高仿真程序的效率。除了带领读者了解这些程序背后的原理之外，本书还介绍了一些经常被应用的技巧和经验，这些技巧和经验在仿真中是“常识”般的存在。掌握它们并了解其背后原理，就可以根据实际问题迅速选择合适的技巧。本书的读者对象是活跃在计算机仿真领域或计划成为计算机仿真领域的人士。
Preface to the Second Edition
Preface
List of Symbols
1 Introduction
Part I Basics
2 Statistical Mechanics
2.1 Entropy and Temperature
2.2 Classical Statistical Mechanics
2.2.1 Ergodicity
2.3 Questions and Exercises
3 Monte Carlo Simulations
3.1 The Monte Carlo Method
3.1.1 Importance Sampling
3.1.2 The Metropolis Method
3.2 A Basic Monte Carlo Algorithm
3.2.1 The Algorithm
3.2.2 Technical Details
3.2.3 Detailed Balance versus Balance
3.3 Trial Moves
3.3.1 Translational Moves
3.3.2 Orientational 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
4.2.1 Initialization
4.2.2 The Force Calculation
4.2.3 Integrating the Equations of Motion
4.3 Equations of Motion
4.3.1 Other Algorithms
4.3.2 Higher-Order Schemes
4.3.3 Liouville Formulation of Time-Reversible Algorithm
4.3.4 Lyapunov Instability
4.3.5 One More Way to Look at the Verlet Algorithm
4.4 Computer Experiments
4.4.1 Diffusio
4.4.2 Order-n Algorithm to Measure Correlations
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
5.2.1 Monte Carlo Simulations
5.2.2 Justification of the Algorithm
5.3 Microcanonical Monte Carlo
5.4 Isobaric-lsothermal Ensemble
5.4.1 Statistical Mechanical Basis
5.4.2 Monte Carlo Simulations
5.4.3 Applications
5.5 Isotension-Isothermal Ensemble
5.6 Grand-Canonical Ensemble
5.6.1 Statistical Mechanical Basis
5.6.2 Monte Carlo Simulations
5.6.3 Justification of the Algorithm
5.6.4 Applications
5.7 Questions and Exercises
6 Molecular Dynamics in Various Ensembles
6.1 Molecular Dynamics at Constant Temperature
6.1.1 The Andersen Thermostat
6.1.2 Nosé-Hoover Thermostat
6.1.3 Nosé-Hoover Chains
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
7.2.1 The Particle Isertion Method
7.2.2 Other Ensembles
7.2.3 Overlapping Distribution Method
7.3 Other Free Energy Methods
7.3.1 Multiple Histograms
7.3.2 Acceptance Ratio Method
7.4 Umbrella Samplin
7.4.1 Nonequilibrium Free Energy Methods
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
8.3.1 Particle Displacement
8.3.2 Volume Change
8.3.3 Particle Exchange
8.3.4 Implementation
8.3.5 Analyzing the Results
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 Itegration
10.2 Free Energies of Solids
10.2.1 Atomic Solids with Continuous Potentials
10.3 Free Energies of Molecular Solids
10.3.1 Atomic Solids with Discontinuous Potentials
10.3.2 General Implementation Issues
10.4 Vacancies and Interstitials
10.4.1 Free Energies
10.4.2 Numerical Calculations
11 Free Energy of Chain Molecules
11.1 Chemical Potential as Reversible Work
11.2 Rosenbluth Sampling
11.2.1 Macromolecules with Discrete Conformations
11.2.2 Extension to Continuously Deformable Molecules
11.2.3 Overlapping Distribution Rosenbluth Method
11.2.4 Recursive Sampling
11.2.5 Pruned-Enriched Rosenbluth Method
Part IV Advanced Techniques
12 Long-Range Interactions
12.1 Ewald Sums
12.1.1 Point Charges
12.1.2 Dipolar Particles
12.1.3 Dielectric Constant
12.1.4 Boundary Conditions
12.1.5 Accuracy and Computational Complexity
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
13.1.1 Beyond Metropolis
13.1.2 Orientational Bias
13.2 Chain Molecules
13.2.1 Configurational-Bias Monte Carlo
13.2.2 Lattice Models
13.2.3 Off-lattice Case
13.3 Generation of Trial Orientations
13.3.1 Strong Intramolecular Interactions
13.3.2 Generation of Branched Molecules
13.4 Fixed Endpoints
13.4.1 Lattice Models
13.4.2 Fully Flexible Chain
13.4.3 Strong Intramolecular Interactions
13.4.4 Rebridging Monte Carlo
13.5 Beyond Polymers
13.6 Other Ensembles
13.6.1 Grand-Canonical Ensemble
13.6.2 Gibbs Ensemble Simulations
13.7 Recoil Growth
13.7.1 Algorithm
13.7.2 Justification of the Method
13.8 Questions and Exercises
14 Accelerating Monte Carlo Sampling
14.1 Parallel Tempering
14.2 Hybrid Monte Carlo
14.3 Cluster Moves
14.3.1 Clusters
14.3.2 Early Rejection Scheme
15 Tackling Time-Scale Problems
15.1 Constraints
15.1.1 Constrained and Unconstrained Averages
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
16.2.1 Computational Aspects
16.3 Diffusive Barrier Crossing
16.4 Transition Path Ensemble
16.4.1 Path Ensemble
16.4.2 Monte Carlo Simulations
16.5 Searching for the Saddle Point
17 Dissipative Particle Dynamics
17.1 Description of the Technique
17.1.1 Justification of the Method
17.1.2 Implementation of the Method
17.1.3 DPD and Energy Conservation
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
A.3.1 Canonical Transformation
A.3.2 Symplectic Condition
A.3.3 Statistical Mechanics
B Non-Hamiltonian Dynamics
B.1Theoretical Background
B.2 Non-Hamiltonian Simulation of the N, V, T Ensemble
B.2.1 The Nosé-Hoover Algorithm
B.2.2 Nosé-Hoover Chains
B.3 The N, P, T Ensemble
C Linear Response Theory
C.1 Static Response
C.2 Dynamic Response
C.3 Dissipation
C.3.1 Electrical Conductivity
C.3.2 Viscosity
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
E.2.1 Canonical Ensemble
E.2.2 The Isothermal-Isobaric Ensemble
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.1.1 Basic Definitions
H.1.2 Free Energy Density
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
Bibliography
Author Index
Index
Why did we write a second edition? A minor revision of the first edition would have been adequate to correct the (admittedly many) typographical mistakes. However, many of the nice comments that we received from students and colleagues alike, ended with a remark of the type: “unfortunately you don’t discuss topic x”. And indeed, we feel that, after only five years the simulation world has changed so much that the title of the book was no
longer covered by the contents. The first edition was written in 1995 and since then several new techniques have appeared or matured. Most (but not all) of the major changes in the second edition deal with these new developments. In particular, we have included a section on:
- Transition path sampling and diffusive barrier crossing to simulate rare events
- Dissipative particle dynamic as a course-grained simulation technique
- Novel schemes to compute the long-ranged forces
- Discussion on Hamiltonian and non-Hamiltonian dynamics in the context of 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
We have updated some of the examples to include also recent work. Several new Examples have been added to illustrate recent applications.
We have taught several courses on Molecular Simulation, based on the first edition of this book. As part of these courses, Dr. Thijs Vlugt prepared many Questions, Exercises, and Case Studies, most of which have been included in the present edition. Some additional exercises can be found on the Web. We are very grateful to Thijs Vlugt for the permission to reproduce this material.
Many of the advanced Molecular Dynamics techniques described in this book are derived using the Lagrangian or Hamilton formulations of classical mechanics. However, many chemistry and chemical engineering students are not familiar with these formalisms. While a full description of classical mechanics is clearly beyond the scope of the present book, we have added an Appendix that summarizes the necessary essentials of Lagrangian and Hamiltonian mechanics.
Special thanks are due to Giovanni Ciccotti, Rob Groot, Gavin Crooks, Thijs Vlugt, and Peter Bolhuis for their comments on parts of the text. In addition, we thank everyone who pointed out mistakes and typos, in particular Drs. J.B. Freund, R. Akkermans, and D. Moroni.
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