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Instructors: Dr. Robinson Cortes Huerto; Dr. Giovanni Settanni; Dr. Omar Valsson
Event type:
Lecture/practice class
Displayed in timetable as:
Mod.Rechenmethoden d
Hours per week:
4
Credits:
6,0
Language of instruction:
Englisch
Min. | Max. participants:
- | -
Requirements / organisational issues:
The course introduces advanced tools and techniques for performing computer simulations. It is a follow up of the introductory course on “Computer Simulations techniques in Statistical Physics”. Familiarity with basic simulation techniques (molecular dynamics or Monte Carlo simulations) is recommended to attend the course.
The lecture will be given in English.
Format: 3V + 1Ü.
Begin: Tuesday, April 16, 10:15, Seminarraum E (Staudinger Weg 9, 1st floor)
Contents:
Introduction to statistical mechanics: Ensembles, error estimates
Free energy calculations: Thermodynamic integration
Histogram reweighting and Bennet's acceptance ratio
Replica Exchange molecular dynamics, weighted histogram analysis
Umbrella sampling; Principal component analysis
Metadynamics
Kinetics: Transition state theory
Kinetics: Hyperdynamics
Kinetics: Markov state models, clustering
Kinetics: Bayesian approaches, commitment probability
Born-Oppenheimer approximation, electronic charge density
Thomas-Fermi approximation to Kohn-Sham DFT
Kohn-Sham equations and basis set expansions
Exchange-correlation functional, derivative discontinuity
Recommended reading list:
Understanding Molecular Simulations, second edition, D. Frenkel & B Smit, Academic Press
Statistical mechanics: Theory and Molecular Simulation, M.E. Tuckermann, Oxford University Press
Additional information:
In SOSe 2020 the course will be conducted as follow, until physical access to the capus will be reestablished:
Lectures notes and exercises will be made available as documents on the reader
https://reader.uni-mainz.de/SoSe2020/08-128-745-00/SitePages/Portal.aspx
A weekly skype session of questions and answers with the teachers will be organized.
To qualify for the oral exam, the students will have to complete bi-weekly exercises (with an average score of more than 50%) and a small final project to be presented at the end of the course.
Digital teaching:
The following reader page will be used to post lecture notes and exercises.
https://reader.uni-mainz.de/SoSe2020/08-128-745-00/SitePages/Portal.aspx
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