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Molecular simulation of fluids : theory, algorithms, object-orientation, and parallel computing /

Molecular simulation allows researchers unique insight into the structures and interactions at play in fluids.Since publication of the first edition of Molecular Simulation of Fluids, novel developments in theory, algorithms and computer hardware have generated enormous growth in simulation capabili...

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Bibliographic Details
Main Author: Sadus, Richard J.
Corporate Author: ScienceDirect (Online service)
Format: eBook
Language:English
Published: Amsterdam : Elsevier, [2024]
Edition:2nd edition.
Subjects:
Fluids > Computer simulation.
Intermolecular forces > Computer simulation.
Molecular dynamics > Computer simulation.
Fluides > Simulation par ordinateur.
Forces intermoléculaires > Simulation par ordinateur.
Dynamique moléculaire > Simulation par ordinateur.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Intro
  • Title page
  • Table of Contents
  • Copyright
  • Dedication
  • Preface to the second edition
  • Preface to the first edition
  • Chapter 1. Introduction
  • Abstract
  • 1.1 What is molecular simulation?
  • 1.2 Progress in molecular simulation
  • References
  • Chapter 2. Ensembles, thermodynamic averages, and particle dynamics
  • Abstract
  • 2.1 Statistical mechanics, ensembles, and averaging
  • 2.2 Properties from fluctuations
  • 2.3 Alternative methods for thermodynamic properties
  • 2.4 Particle dynamics
  • 2.5 Summary
  • References
  • Chapter 3. Intermolecular pair potentials and force fields
  • Abstract
  • 3.1 Calculation of the potential energy
  • 3.2 Intermolecular forces
  • 3.3 Potentials with a hard sphere contribution
  • 3.4 Soft sphere potentials
  • 3.5 Fully interpenetrable potentials
  • 3.6 Effective pairwise potentials for atoms and simple molecules
  • 3.7 Contributions to molecular interactions
  • 3.8 Simple atom-based pairwise potentials for molecules
  • 3.9 Extension to molecules
  • 3.10 Pairwise force fields from molecular mechanics
  • 3.11 Case study: Models for water
  • 3.12 Application to mixtures
  • 3.13 Summary
  • References
  • Chapter 4. Ab initio, two-body and three-body intermolecular potentials
  • Abstract
  • 4.1 Ab initio calculations
  • 4.2 Two-body atomic potentials
  • 4.3 Three-body atomic potentials
  • 4.4 Four- and higher-body atomic interactions
  • 4.5 Potentials for molecules
  • 4.6 Case study: Augmenting ab initio potentials for fluid properties
  • 4.7 Summary
  • References
  • Chapter 5. Calculating molecular interactions
  • Abstract
  • 5.1 Calculation of short-range interactions
  • 5.2 Calculation of long-range interactions
  • 5.3 Summary
  • References
  • Chapter 6. Monte Carlo simulation
  • Abstract
  • 6.1 Basic concepts
  • 6.2 Application to molecules
  • 6.3 Advanced techniques.
  • 6.4 Path integral Monte Carlo
  • 6.5 Summary
  • References
  • Chapter 7. Integrators for molecular dynamics
  • Abstract
  • 7.1 Integrating the equations of motion
  • 7.2 Gear predictor-corrector methods
  • 7.3 Verlet predictor methods
  • 7.4 Runge-Kutta integration
  • 7.5 Comparison of integrators
  • 7.6 Integrators for molecules
  • 7.7 Summary
  • References
  • Chapter 8. Nonequilibrium molecular dynamics
  • Abstract
  • 8.1 An example NEMD algorithm
  • 8.2 Synthetic NEMD algorithms
  • 8.3 Application of NEMD algorithms to molecules
  • 8.4 Application of NEMD algorithms to mixtures
  • 8.5 Comparison with EMD
  • 8.6 Summary
  • References
  • Chapter 9. Molecular simulation of ensembles
  • Abstract
  • 9.1 Monte Carlo
  • 9.2 Molecular dynamics
  • 9.3 Summary
  • References
  • Chapter 10. Molecular simulation of phase equilibria
  • Abstract
  • 10.1 Calculating the chemical potential
  • 10.2 Gibbs ensemble Monte Carlo
  • 10.3 MD Gibbs ensemble
  • 10.4 NpT + test particle
  • 10.5 Gibbs-Duhem integration
  • 10.6 Thermodynamic scaling
  • 10.7 Pseudo-ensemble methods
  • 10.8 Histogram reweighting algorithms
  • 10.9 Finite-size scaling
  • 10.10 Empirically based simulation algorithms
  • 10.11 Accurate determination of SLE
  • 10.12 Summary
  • References
  • Chapter 11. Molecular simulation and object-orientation
  • Abstract
  • 11.1 Fundamental concepts of OO
  • 11.2 Case study: application of OO to the microcanonical MD simulation of Lennard-Jones atoms
  • 11.3 Case study: application of object orientation to the microcanonical MC simulation of Lennard-Jones atoms
  • 11.4 Case study: combined MD and MC program for Lennard-Jones atoms in the microcanonical ensemble
  • 11.5 Case study: extensions
  • 11.6 Summary
  • References
  • Chapter 12. GPU and CPU parallel molecular simulation with CUDA and MPI
  • Abstract
  • 12.1 Basic GPU implementations with CUDA.
  • 12.2 Case study: CUDA implementation of MD code (/mdCU_Ver2.0)
  • 12.3 Case study: CUDA implementation of MC code (/mcCU_Ver2.0)
  • 12.4 Parallel programming with MPI
  • 12.5 Case study: MPI implementation of MD code (/mdMPI_Ver2.0)
  • 12.6 Case study: MPI implementation of MC code (/mcMPI_Ver2.0)
  • 12.7 Case study: relative performance of MD and MC MPI and GPU codes
  • 12.8 Summary
  • References
  • Appendix 1. Software user's guide
  • A.1 C++ code for the molecular dynamics simulation of
  • A.2 C++ code for the Monte Carlo simulation of Lennard-Jones
  • A.3 C++ code for the combined Monte Carlo and molecular
  • A.4 CUDA code for the molecular dynamics simulation of
  • A.5 CUDA code for the Monte Carlo simulation of
  • A.6 MPI code for the molecular dynamics simulation of
  • A.7 MPI code for the molecular dynamics simulation of
  • A.8 Compilation
  • Appendix 2. List of algorithms
  • Appendix 3. Notation
  • Acronyms and abbreviations
  • Latin alphabet
  • Greek alphabet
  • Subscripts and superscripts
  • Index.