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Molecular Modelling and Simulation - P403001

Title:	Molekulární modelování a simulace
Guaranteed by:	Department of Physical Chemistry (403)
Faculty:	Faculty of Chemical Engineering
Actual:	from 2019 to 2022
Semester:	summer
Points:	summer s.:0
E-Credits:	summer s.:0
Examination process:	summer s.:
Hours per week, examination:	summer s.:2/1, other [HT]
Capacity:	unlimited / unlimited (unknown)
Min. number of students:	unlimited
Qualifications:
State of the course:	taught
Language:	Czech
Teaching methods:	full-time
Level:
Additional information:	http://old.vscht.cz/fch/cz/pomucky/kolafa/N403027.html
Note:	course is intended for doctoral students only can be fulfilled in the future

Guarantor:	Kolafa Jiří prof. RNDr. CSc.
Is interchangeable with:	AP403001

Examination dates

Annotation -

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.

Last update: Matějka Pavel (16.06.2019)

Literature -

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);

Last update: Řehák Karel (17.10.2018)

Syllabus -

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.

Last update: Řehák Karel (05.11.2018)

Learning resources -

http://old.vscht.cz/fch/en/tools/kolafa/S403027.html

Last update: Kubová Petra (12.04.2018)

Learning outcomes -

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

Last update: Kolafa Jiří (08.08.2018)

Entry requirements -

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

Last update: Kolafa Jiří (08.08.2018)