SubjectsSubjects(version: 877)
Course, academic year 2020/2021
Molecular Modelling and Simulation - P403001
Title: Molekulární modelování a simulace
Guaranteed by: Department of Physical Chemistry (403)
Actual: from 2019
Semester: summer
Points: summer s.:0
E-Credits: summer s.:0
Examination process: summer s.:
Hours per week, examination: summer s.:2/1 other [hours/week]
Capacity: unknown / unknown (unknown)
Min. number of students: unlimited
Language: Czech
Teaching methods: full-time
For type: doctoral
Additional information:
Note: course is intended for doctoral students only
can be fulfilled in the future
Guarantor: Kolafa Jiří prof. RNDr. CSc.
Interchangeability : D403010
Z//Is interchangeable with: AP403001
Annotation -
Last update: Matějka Pavel prof. Dr. RNDr. (16.06.2019)
Basics of modeling of molecules (and other many-particle systems) by means of classical statistical mechanics, from force field construction to molecular dynamics and Monte Carlo simulations. Emphasis is on the methodology of a computer experiment (pseudoexperiment). An individual simulation project of every Ph.D. student is required, either developing a code for a simple system or using a simulation package. Edu-software is available.
Aim of the course -
Last update: Kolafa Jiří prof. RNDr. CSc. (08.08.2018)

Students will:

  • Understand the principles of molecular modeling and simulation in the frame classical and quantum thermodynamics
  • Have good overview of MC and MD simulation methods including determination of various quantities
  • Be able to perform a simulation using a suitable package, optionally using own computer code

Literature -
Last update: Řehák Karel doc. Ing. CSc. (17.10.2018)

R: D. Frenkel a B. Smit: Understanding Molecular Simulation (Academic Press, 1996, 2002);

A: M. P. Allen a D. J. Tildesley: Computer Simulation of Liquids (Clarendon Press, Oxford 1986, 2002);

Learning resources -
Last update: Kubová Petra Ing. (12.04.2018)

Syllabus -
Last update: Řehák Karel doc. Ing. CSc. (05.11.2018)

1. Introduction - What are simulations good for?

2. Repetition of statistical thermodynamics and less common ensembles (isobaric).

3. Atomistic and lattice models. Force field.

4. Molecular dynamics: Verlet's method, leap-frog. Fundamentals of Hamilton's mechanics, conservation laws. Symplecticity.

5. Other integrators (Gear, multiple timestep). Thermostats in MD.

6. Monte Carlo Methods - MC integration, Metropolis method. Random numbers.

7. Methodology of simulations and measurement of quantities, statistical errors. Boundary conditions.

8. Structural quantities: radial distribution functions, structure factor.

9. Entropic quantities: thermodynamic integration, non-Boltzmann sampling, integration of mean force, Widom's method.

10. Potential range, cutoff corrections. Coulomb's forces: Ewald summation, reaction field.

11. Other ensembles: isobaric, grandkanonical, Gibbs. Additional degrees of freedom in MD: Nose-Hoover, barostat.

12. Other MC methods: preferential sampling, molecules, polymers. Constraint dynamics (SHAKE). Optimization of simulations.

13. Brownian (Langevin) dynamics and DPD. Kinetic quantities: EMD vs. NEMD.

14. Optimization: simulated annealing, genetic algorithms.

Entry requirements -
Last update: Kolafa Jiří prof. RNDr. CSc. (08.08.2018)

Good knowledge of chemical and statistical thermodynamics. Basic knowledge of theoretical mechanics is recommended.