Computer Simulations in Statistical Physics
This lecture/hands-on course provides an introduction to computer simulation methods in statistical physics. The course targets both bachelor and master students familiar with statistical mechanics.
Outline
- Ensembles, partition function [Kardar]
- Error estimates [Janke]
- Molecular dynamics [Frenkel and Smit]
- Integration algorithms [Frenkel and Smit]
- Accuracy, time-reversibility [Frenkel and Smit]
- MD trajectories, analysis
- Thermostats [Frenkel and Smit, Tuckerman]
- Barostats [Frenkel and Smit, Tuckerman]
- Long-range interactions, Ewald summation [Tuckerman]
- Classical force-fields
- Hands-on: introduction to GROMACS
- MD for biomolecular simulations
- Monte Carlo: importance sampling [Tuckerman]
- Canonical, grand canonical simulations [Tuckerman]
- Master Equation [Jansen]
- Kinetic Monte Carlo [Jansen]
- Advanced force-fields: multipole expansion [Stone]
- Polarization, Thole model [Stone]
- Van der Waals Interactions [Stone]
- Systematic coarse-graining [Noid]
Literature
- Mehran Kardar, Statistical physics of particles
- Wolfhard Janke, Statistical analysis and simulations: data correlations and error estimation
- Mark E. Tuckerman, Statistical Mechanics: Theory and Molecular Simulation
- Anthony Stone, The Theory of Intermolecular Forces
- A. P. J. Jansen, An introduction to Monte Carlo simulations of surface reactions
- W. G. Noid, Perspective: Coarse-grained models for biomolecular systems J. Chem. Phys. 139 090901 (2013)
- Daan Frenkel and Berend Smit, Understanding Molecular Simulation: From Algorithms to Applications