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分子模擬:從算法到應(yīng)用(第2版 英文版)

分子模擬:從算法到應(yīng)用(第2版 英文版)

定 價:¥129.00

作 者: 〔荷〕達恩 · 弗倫克爾(Daan Frenkel)〔荷〕貝倫德 · 斯密特(Berend Smit)
出版社: 世界圖書出版公司
叢編項:
標 簽: 暫缺

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ISBN: 9787523218570 出版時間: 2025-04-01 包裝: 平裝-膠訂
開本: 16開 頁數(shù): 字數(shù):  

內(nèi)容簡介

  由達恩·弗倫克爾和貝倫德·斯密特所著的《分子模擬:從算法到應(yīng)用》解釋了材料科學分子模擬背后的物理學。由于計算機模擬人員一直面臨著為特定應(yīng)用選擇特定技術(shù)的問題,并且現(xiàn)有的工具種類繁多,所以在選擇技術(shù)時需要充分了解基本原理。更重要的是,這種對基本原理的理解可以大大提高仿真程序的效率。除了帶領(lǐng)讀者了解這些程序背后的原理之外,本書還介紹了一些經(jīng)常被應(yīng)用的技巧和經(jīng)驗,這些技巧和經(jīng)驗在仿真中是“常識”般的存在。掌握它們并了解其背后原理,就可以根據(jù)實際問題迅速選擇合適的技巧。本書的讀者對象是活躍在計算機仿真領(lǐng)域或計劃成為計算機仿真領(lǐng)域的人士。

作者簡介

  達恩·弗倫克爾(Daan Frenkel)是劍橋大學化學名譽教授,曾任劍橋大學化學系主任和研究主任。分別在1972年和1977年于荷蘭阿姆斯特丹大學獲得物理化學的碩士和博士學位。1999年獲得英國皇家化學學會伯克講師和獎?wù)隆?007年榮獲歐洲物理學會 Alder-CECAM 計算機模擬獎。2008年當選美國人文與科學院外籍榮譽院士。2016年當選美國國家科學院外籍院士,同年獲得玻爾茲曼獎?wù)拢↖UPAP)。 貝倫德·斯密特(Berend Smit)是瑞士洛桑聯(lián)邦理工學院(EPFL)化學工程與化學教授、化學與生物分子工程兼職教授、美國加州大學伯克利分?;瘜W兼職教授。他于1987年獲得荷蘭代爾夫特理工大學化學工程碩士學位和物理學碩士學位。1990年獲得荷蘭烏得勒支大學化學博士學位。1997—2007 年在荷蘭阿姆斯特丹大學擔任計算化學教授。2004年,他當選為法國里昂歐洲原子與分子計算中心(CECAM)主任。自 2007 年起,他擔任加州大學伯克利分?;瘜W工程和化學教授,以及勞倫斯伯克利國家實驗室材料科學部的化學教員。自 2014 年以來,他一直擔任 EPFL 能源中心主任。

圖書目錄

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

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