Difference between revisions of "Simulation Techniques for Soft Matter Sciences (SS 2007)"
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| Monte-Carlo integration/simulation (Simple vs. Importance sampling, Random walks (RW) and Self-avoiding random walks (SAW)) | | Monte-Carlo integration/simulation (Simple vs. Importance sampling, Random walks (RW) and Self-avoiding random walks (SAW)) | ||
− | Look at Zuse's Z3 computer from 1941: [http://upload.wikimedia.org/wikipedia/de/4/4c/Z3_Deutsches_Museum.JPG Z3] | + | Look at Zuse's Z3 computer from 1941: [http://upload.wikimedia.org/wikipedia/de/4/4c/Z3_Deutsches_Museum.JPG Z3] and |
+ | read something about the first big US computer at Los Alamos [http://www.lanl.gov/history/atomicbomb/computers.shtml Evolving from Calculators to Computers] | ||
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Revision as of 11:28, 19 April 2007
Overview
- Type
- Lecture (2 SWS) and Tutorials (2 SWS)
- Lecturer
- PD Dr. Christian Holm (Lecture) and working group (Tutorials)
- Course language
- English
- Time and Room
- Lecture: Thu 12:00 - 14:00, Phys 1.114
Tutorials: will be discussed in first lecture
Soft matter science is the science of "soft" materials, like polymers, liquid crystals, colloidal suspensions, ionic solutions, hydrogels and most biological matter. The phenomena that define the properties of these materials occur on mesoscopic length and time scales, where thermal fluctuations play a major role. These scales are hard to tackle both experimentally and theoretically. Instead, computer simulations and other computational techniques play a major role.
The course will give an introduction to the computational tools that are used in soft matter science, like Monte-Carlo (MC) and Molecular dynamics (MD) simulations (on- and off-lattice) and Poisson-Boltzmann theory (and extensions).
Prerequisites
The course is intended for participants in the Master Program "Computational Science", but should also be useful for FIGSS students and for other interested science students.
We expect the participants to have basic knowledge in classical and statistical mechanics, thermodynamics, electrodynamics, and partial differential equations, as well as knowledge of a programming language (preferably C or C++).
Lecture and tutorials
The lecture is accompanied by hands-on-tutorials which will be held in the computer room (Physics, 1.120). They consist of practical excercises at the computer, like small programming tasks, simulations, visualisation and data analysis.
The tutorials build on each other, therefore continous attendance is expected.
The dates of the tutorials will be scheduled in the first lecture.
Lecture
Date | Subject |
---|---|
19.4. | Monte-Carlo integration/simulation (Simple vs. Importance sampling, Random walks (RW) and Self-avoiding random walks (SAW))
Look at Zuse's Z3 computer from 1941: Z3 and read something about the first big US computer at Los Alamos Evolving from Calculators to Computers |
26.4. | 2D Ising model I (Phase transitions, Critical phenomena, Finite size scaling) |
3.5. | 2D Ising model II (Reweighting, Cluster Algorithm) |
10.5. | Error Analysis (Binning, Jackknife, ...) |
17.5. | Holiday |
24.5. | Molecular Dynamics I (Velocity Verlet algorithm, Reduced units, Langevin thermostat, Potentials, Forces, Atomistic force fields) |
31.5. | Molecular Dynamics II |
7.6. | Holiday |
14.6. | Long range interactions (Direct sum, Ewald summation, P3M, Fast Multipole method) |
21.6. | Simulations of Polymers and Polyelectrolytes |
28.6. | Poisson-Boltzmann Theory |
5.7. | Introduction to the Project work: charged infinite rods in ionic solution |
12.7. | Extended tutorial I: project work |
19.7. | Extended tutorial II: project work |
Tutorials
Materials on the tutorials can be found behind the links!
Date | Subject | Tutors |
---|---|---|
19.4. | Introductory tutorial | Kai Grass |
26.4. | Random walks | Kai Grass |
3.5. | Monte Carlo: The Ising model I | Marcello Sega |
10.5. | Monte Carlo: The Ising model II | Marcello Sega |
17.5. | Holiday | |
24.5. | Error analysis | Joan Jose Cerdà |
31.5. | Molecular Dynamics: Lennard-Jones liquid | Qiao Baofu |
7.6. | Holiday | |
14.6. | Long range interactions: Direct sum and Ewald summation | Joan Jose Cerdà |
21.6. | Introduction to MD simulations with ESPResSo | Mehmet Süzen |
28.6. | Visualisation of MD simulations with VMD | Olaf Lenz |
5.7. | Simulation of polymers and polyeletrolytes | Qiao Baofu |
12.7. | Extended tutorial I: project work | Olaf Lenz and Mehmet Süzen |
19.7. | Extended tutorial II: project work | Olaf Lenz and Mehmet Süzen |
Recommended literature
<bibcite>frenkel02b,allen87a</bibcite>