260010 LP Laboratory: Computational Materials Physics (2025S)
Continuous assessment of course work
Labels
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Mo 03.02.2025 08:00 to Mo 24.02.2025 23:59
- Deregistration possible until Fr 21.03.2025 23:59
Details
max. 4 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- Thursday 06.03. 13:15 - 18:15 Seminarraum 18 Kolingasse 14-16, OG02
- Thursday 13.03. 13:15 - 18:15 Seminarraum 18 Kolingasse 14-16, OG02
- Thursday 20.03. 13:15 - 18:15 Seminarraum 18 Kolingasse 14-16, OG02
- Thursday 27.03. 13:15 - 18:15 Seminarraum 18 Kolingasse 14-16, OG02
- Thursday 03.04. 13:15 - 18:15 Seminarraum 18 Kolingasse 14-16, OG02
- Thursday 10.04. 13:15 - 18:15 Seminarraum 18 Kolingasse 14-16, OG02
- Thursday 08.05. 13:15 - 18:15 Seminarraum 18 Kolingasse 14-16, OG02
- Thursday 15.05. 13:15 - 18:15 Seminarraum 18 Kolingasse 14-16, OG02
- Thursday 22.05. 13:15 - 18:15 Seminarraum 18 Kolingasse 14-16, OG02
- Thursday 05.06. 13:15 - 18:15 Seminarraum 18 Kolingasse 14-16, OG02
- Thursday 12.06. 13:15 - 18:15 Seminarraum 18 Kolingasse 14-16, OG02
Information
Aims, contents and method of the course
Assessment and permitted materials
The performance will be assessed in regular meeting with the supervisor. The developed code as well as a report (5-10 pages in the case of code development, 10-20 pages in the case of using an existing code) need to be handed in.
Minimum requirements and assessment criteria
Completion of the project.
Examination topics
Exam-immanent course.
Reading list
https://www.vasp.at/wiki
Computational Physics, Jos Thijssen, https://doi.org/10.1017/CBO9781139171397
Computational Physics, Jos Thijssen, https://doi.org/10.1017/CBO9781139171397
Association in the course directory
M-VAF A 2, M-VAF B, PM-SPEC
Last modified: Fr 30.01.2026 09:26
Methods: first principles codes (e.g. VASP), Quantum Monte Carlo methods, Machine Learning