Addressing the need of chemistry, biology and engineering students to understand and perform their own molecular simulations, the author introduces the fundamentals of molecular modeling for a broad, practice–oriented audience and presents versatile practical applications. The book presents a thorough overview of the underlying concepts. Starting out with Newton?s equations, it moves on to force–field methods for modelling potential energy surfaces. The author gives an account of probability concepts and subsequently introduces statistical and quantum mechanics. In addition to Monte–Carlo methods, the core of the text covers molecular dynamics simulations in detail and shows how to derive critical physical parameters. The background knowledge required in physics, chemistry and mathematics is given when needed and together with the simulation methodology. The whole is rounded off with a look at advanced techniques and gives invaluable advice on how to set up simulations for a diverse range of applications, preparing readers for their own endeavors in this exciting field. INDICE: 1. INTRODUCTION . Motivation: Relating Microscopic Molecular Properties to Macroscopic /. Thermodynamic Behavior of Bulk Systems. Molecular Dynamics and Monte Carlo Simulations for Modeling Macroscopic Scale Experiment. Scope of the Book and Background Needed. . 2. CLASSICAL MECHANICS FOR MANY–MOLECULE SYSTEMS. Newton?s Equations of Motion for Many–Molecule Systems. Analytical Solution of Newton?s Equations for Simple Systems. The Concept of Trajectory and Phase Space. Numerical Solution of Newton?s Equations, Finite–Difference Method. The Verlet and Leapfrog Time Progression Algorithms. The Trajectory for a Many–Particle System. . 3. FORCE FIELD MODELS IN CLASSICAL MOLECULAR SIMULATIONS. Summary of the Quantum Mechanics of Molecular Force Fields. Modeling the Potential Energy Surface of Non–Reactive Systems. Inter– and Intra–Molecular Force Fields. Design and Choice of Force Fields. Calculation of the Force Field Parameters Using Quantum Chemistry, Spectroscopy, and Modeling. . 4. INTRODUCTION TO PROBABILITY CONCEPTS. Basic Concepts of Probability Theory: Single and Multiple Variable Probabilities, Discrete and Continuous Probabilities, the Central Limit Theorem. Maxwell–Boltzmann Probability Distribution for Molecular Velocity, Speed. Maxwell–Boltzmann Energy Distribution for Single Molecules and Collections of Molecules. Probability Distributions of Large Collections of Molecules. Assigning Molecular Velocities in Molecular Simulations from the Maxwell–Boltzmann Probability Distribution. . 5. INTRODUCTION TO STATISTICAL MECHANICS. Statistical Mechanics in Classical Mechanics Language. The Concept of Partition Function and Ensembles in Statistical Mechanics: . Canonical (Isothermal Isochoric), Isothermal Isobaric, Grand–Canonical and Other Ensembles. Thermodynamic Properties: Energy, Temperature, Pressure, Entropy, Free Energy, Fluctuations in These Quantities in Microscopic Systems. The Quantum Mechanical Approach to Statistical Mechanics. . 6. MOLECULAR DYNAMICS (MD) SIMULATIONS 1. Periodic Boundary Conditions: Simulating Infinite Bulk Systems with a Finite Number of Molecules. Simulating Bulk Phases, Surfaces, and Nanoparticles; . Short–Range Van Der Waals Forces: Truncation of Potentials. Long–Range Electrostatic Forces: Ewald Summations. . 7. MOLECULAR DYNAMICS (MD) SIMULATIONS 2. Including the Effect of the Environment in Molecular Simulations: Thermostats and Barostats. Determining Thermodynamic Averages from Molecular Dynamics Trajectories: Multiple Time Origins and Maximizing Sample Size and Statistical Averaging. . 8. ANALYZING MOLECULAR DYNAMICS SIMULATIONS. Characterizing the Microscopic Structure of Phases. Radial Distribution Functions, Order Parameters. Dynamics of Molecules from MD. Mean–Square Displacements, Velocity Autocorrelations, Diffusion Coefficients. . 9. MONTE CARLO (MC) SIMULATION METHODS AND APPLICATIONS. Principles of Monte Carlo Methods, Sampling from the Ensemble Probability Distribution. Importance Sampling, Microscopic Reversibility. Advantage and Disadvantages of Monte Carlos Simulations in Comparison to MD Simulations. Simulations in Different Ensembles with MC. . 10. ADVANCED MOLECULAR SIMULATION TECHNIQUES. Free Energy from Molecular Simulations. Replica Exchange and Improving Phase Space Sampling. Course–Graining of Potentials. . 11. OTHER BACKGROUND KNOWLEDGE REQUIRED FOR RUNNING MOLECULAR SIMULATIONS. Setting Up the Initial Geometry: Solids, Space Groups and Symmetry, Liquids and Gases. Selecting and Setting Up the Force Field for a Molecular Simulation. Simulations of Proteins, DNA, and RNA. Simulations of Biological Membranes. The PDB Format for Initial Geometry
- ISBN: 978-3-527-34105-4
- Editorial: Wiley VCH
- Encuadernacion: Rústica
- Páginas: 450
- Fecha Publicación: 20/09/2017
- Nº Volúmenes: 1
- Idioma: Inglés