Understanding Molecular Simulation
Understanding Molecular Simulation
From Algorithms to Applications
Smit, Berend; Frenkel, Daan
Elsevier Science & Technology
07/2023
679
Mole
Inglês
9780323902922
15 a 20 dias
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
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