|
Size: 31
Comment:
|
Size: 1482
Comment:
|
| Deletions are marked like this. | Additions are marked like this. |
| Line 1: | Line 1: |
| Describe Main/WS22_MLQc here. | = Seminar on Machine Learning for Quantum Chemistry = || Date || Kickoff meeting: Tuesday, November 15st 2022, 12:15-12:45 || || || Presentation: January 25th (time TBA, depending on the amount of participants) || || Room || Hybrid: MAR 4.033 or Zoom (https://tu-berlin.zoom.us/j/6090057450?pwd=dEFRWXhvdTB5OGhzMnBQdjBid1gxdz09) || || ISIS || https://isis.tu-berlin.de/course/view.php?id=31905 || || Trainers || Stefaan Hessmann, Niklas Gebauer || || Contact || stefaan.hessmann@tu-berlin.de, n.gebauer@tu-berlin.de || || Credits || 3 ECTS, Elective in the modules "Machine Learning I", "Machine Learning II", and "Cognitive Algorithms" || This is a research-oriented seminar about applications of machine learning to quantum chemistry. There is no formal registration for the kick-off meeting. In the general case, it is not possible to take the seminar as a standalone course. Students will read, understand, evaluate and present selected research papers on machine learning methods in quantum chemistry. At the end of the semester, each student will present their topic in a 20 min talk (+ 10 min questions) in English. For a list of available research papers, please refer to the ISIS course linked above. The available topics cover * Representations of molecules and materials * Encoding prior knowledge and symmetries in ML models * Solving the Schrödinger equation with ML * ML for molecular dynamics * Generative models for molecules |
Seminar on Machine Learning for Quantum Chemistry
Date |
Kickoff meeting: Tuesday, November 15st 2022, 12:15-12:45 |
|
Presentation: January 25th (time TBA, depending on the amount of participants) |
Room |
Hybrid: MAR 4.033 or Zoom (https://tu-berlin.zoom.us/j/6090057450?pwd=dEFRWXhvdTB5OGhzMnBQdjBid1gxdz09) |
ISIS |
|
Trainers |
Stefaan Hessmann, Niklas Gebauer |
Contact |
|
Credits |
3 ECTS, Elective in the modules "Machine Learning I", "Machine Learning II", and "Cognitive Algorithms" |
This is a research-oriented seminar about applications of machine learning to quantum chemistry. There is no formal registration for the kick-off meeting. In the general case, it is not possible to take the seminar as a standalone course.
Students will read, understand, evaluate and present selected research papers on machine learning methods in quantum chemistry. At the end of the semester, each student will present their topic in a 20 min talk (+ 10 min questions) in English.
For a list of available research papers, please refer to the ISIS course linked above.
The available topics cover
- Representations of molecules and materials
- Encoding prior knowledge and symmetries in ML models
- Solving the Schrödinger equation with ML
- ML for molecular dynamics
- Generative models for molecules