SubjectsSubjects(version: 949)
Course, academic year 2023/2024
Molecular Modelling and Simulation - AP403001
Title: Molecular Modelling and Simulation
Guaranteed by: Department of Physical Chemistry (403)
Faculty: Faculty of Chemical Engineering
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 [HT]
Capacity: unknown / unknown (unknown)
Min. number of students: unlimited
Language: English
Teaching methods: full-time
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 : P403001
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: Pátková Vlasta (08.06.2018)

Students will understand the principles of molecular modeling and simulation in the frame of quantum and classical thermodynamics. They will master MC and MD simulation methods including determination of various quantities. The students will be able to devise a computer experiment.

Literature -
Last update: Pátková Vlasta (08.06.2018)

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

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

Learning resources -
Last update: Pátková Vlasta (08.06.2018)

Syllabus -
Last update: Pátková Vlasta (08.06.2018)

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: Pátková Vlasta (08.06.2018)

Good knowledge of chemical and statistical thermodynamics, basic knowledge of theoretical mechanics

Registration requirements -
Last update: Kolafa Jiří prof. RNDr. CSc. (20.08.2018)

N403023 Statistical thermodynamics, molecular modeling and simulation