SoSe 14: Computational Molecular Physics and Methods of Molecular Simulations
Petra Imhof
Comments
This module teaches the theoretical basics and simulation techniques for simple stochastic systems (e.g. molecular models, ising models, diffusion in model potentials). Physical principles for ... read more
This module teaches the theoretical basics and simulation techniques for simple stochastic systems (e.g. molecular models, ising models, diffusion in model potentials). Physical principles for stochastic trajectories and ensembles are combined with simulation techniques that are able to generate appropriate data. In more detail, we will cover: - Statistical mechanics: basis and derivations to the most important physical ensembles. Boltzmann distribution, Partition function, Expectations - Monte-Carlo simulation: Theory, construction, convergence and implementation of Monte Carlo methods for the computation of stationary expectation values - Kinetics: Rate theories, time correlations and other time-dependent expectations - Molecular dynamics simulation: Theory, construction, convergence and implementation of MD simulations for the computation of dynamical expectation values
- Emirical energy function - Exploring the energy function: minimization, finding saddle points, vibrational analysis - Algorithms and data structures: periodic boundary conditions, cutoff, efficient neighbor search - Long-ranged interactions: coulomb sum, convergence, Poisson equation, Ewald summation, Particle-Mesh methods - Solvation methods: Explicit and implicit solvation - Dynamics: Integrators, discretization errors
