Understanding Molecular Simulation: From Algorithms to Applications
Understanding Molecular Simulation explains molecular simulation from a chemical-physics and statistical-mechanics perspective. It highlights how physical concepts are used to develop better algorithms and expand the range of applicability of simulations. Understanding Molecular Simulation is equally relevant for those who develop new code and those who use existing packages. Both groups are continuously confronted with the question of which computational technique best suits a given application. Understanding Molecular Simulation provides readers with the foundational knowledge they need to learn about, select and apply the most appropriate of these tools to their own work. The implementation of simulation methods is illustrated in pseudocodes, and their practical use is shown via case studies presented throughout the text.

Since the second edition’s publication, the simulation world has expanded significantly: existing techniques have continued to develop, and new ones have emerged, opening up novel application areas. This new edition aims to describe these new developments without becoming exhaustive; examples are included that highlight current uses, and several new examples have been added to illustrate recent applications. Examples, case studies, questions, and downloadable algorithms are also included to support learning. No prior knowledge of computer simulation is assumed.

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Understanding Molecular Simulation: From Algorithms to Applications
Understanding Molecular Simulation explains molecular simulation from a chemical-physics and statistical-mechanics perspective. It highlights how physical concepts are used to develop better algorithms and expand the range of applicability of simulations. Understanding Molecular Simulation is equally relevant for those who develop new code and those who use existing packages. Both groups are continuously confronted with the question of which computational technique best suits a given application. Understanding Molecular Simulation provides readers with the foundational knowledge they need to learn about, select and apply the most appropriate of these tools to their own work. The implementation of simulation methods is illustrated in pseudocodes, and their practical use is shown via case studies presented throughout the text.

Since the second edition’s publication, the simulation world has expanded significantly: existing techniques have continued to develop, and new ones have emerged, opening up novel application areas. This new edition aims to describe these new developments without becoming exhaustive; examples are included that highlight current uses, and several new examples have been added to illustrate recent applications. Examples, case studies, questions, and downloadable algorithms are also included to support learning. No prior knowledge of computer simulation is assumed.

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Understanding Molecular Simulation: From Algorithms to Applications

Understanding Molecular Simulation: From Algorithms to Applications

Understanding Molecular Simulation: From Algorithms to Applications
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Understanding Molecular Simulation: From Algorithms to Applications

Understanding Molecular Simulation: From Algorithms to Applications

Overview

Understanding Molecular Simulation explains molecular simulation from a chemical-physics and statistical-mechanics perspective. It highlights how physical concepts are used to develop better algorithms and expand the range of applicability of simulations. Understanding Molecular Simulation is equally relevant for those who develop new code and those who use existing packages. Both groups are continuously confronted with the question of which computational technique best suits a given application. Understanding Molecular Simulation provides readers with the foundational knowledge they need to learn about, select and apply the most appropriate of these tools to their own work. The implementation of simulation methods is illustrated in pseudocodes, and their practical use is shown via case studies presented throughout the text.

Since the second edition’s publication, the simulation world has expanded significantly: existing techniques have continued to develop, and new ones have emerged, opening up novel application areas. This new edition aims to describe these new developments without becoming exhaustive; examples are included that highlight current uses, and several new examples have been added to illustrate recent applications. Examples, case studies, questions, and downloadable algorithms are also included to support learning. No prior knowledge of computer simulation is assumed.

Product Details

ISBN-13: 9780323902922
Publisher: Elsevier Science
Publication date: 07/18/2023
Edition description: 3rd ed.
Pages: 679
Product dimensions: 6.00(w) x 9.00(h) x (d)

About the Author

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.

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.

Table of Contents

1. Introduction

Part I: Basics

2. Thermodynamics and Statistical Mechanics

3. Monte Carlo Simulations　

4. Molecular Dynamics Simulations　

5. Computer Experiments

Part II: Ensembles

6. Monte Carlo Simulations in Various Ensembles　

7. Molecular Dynamics in Various Ensembles　

Part III: Free -Energy Calculations

8. Free Energy Calculations

9. Free Energies of Solids　

10. Free Energy of Chain Molecules　

Part IV: Advanced Techniques

11. Long-Ranged Interactions

12. Configurational Bias Monte Carlo

13. Accelerating Monte Carlo Sampling　

14. Time-Scale-separation Problems in MD

15. Rare Events

16. Mesoscopic Fluid Models

Part V: Appendices

A: Lagrangian and Hamiltonian

B: Non-Hamiltonian Dynamics

C: Non-equilibrium Thermodynamics

D: Smoothed Dissipative Particle Dynamics

E: Committor for 1-D diffusive barrier crossing

F: Linear Response Theory: examples

G: Statistical Errors

H: Integration Schemes

I: Saving CPU Time

J: Reference States

K: Statistical Mechanics of the Gibbs "Ensemble"

L: Overlapping Distribution for Polymers

M: Some General Purpose Algorithms

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