Sebastien Röcken
| Adresse | Multiscale Modeling of Fluid Materials Maschinenwesen, TUM Boltzmannstr. 15 85748 Garching bei Müchen, Deutschland |
| Büro | Gebäude 1, 2. Stock, Raum 5501.02.128 |
| Telefon | +49 (89) 289 - 55301 |
| s.roecken(at)tum.de |
Folge mir auf
Research und Interessen
- Machine Leaning in Molecular Dynamics
- Multiscale Modeling and Simulations
Ausbildung
| 2020 | M.Sc. in Mechanical Engineering Technische Universität München, Deutschland |
| 2017 | B.Sc. in Mechanical Engineering |
Publikationen
- Fuchs, P., Thaler, S., Röcken, S., & Zavadlav, J. (2024). chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics. (Sept 2024) arXiv preprint arXiv:2408.15852.
- Poster Presentation at PASC24 on "Accurate machine learning force fields via experimental and simulation data fusion" (June 2024)
- Röcken, Sebastien, Anton F. Burnet, and Julija Zavadlav. Predicting solvation free energies with an implicit solvent machine learning potential. https://arxiv.org/abs/2406.00183
- Invited talk at SIAM Conference on Mathematical Aspects of Materials Science (MS24) on "Deep Molecular Modeling via Experimental and Simulation Data Fusion" (May 2024)
- Röcken, Sebastien, and Julija Zavadlav. Accurate machine learning force fields via experimental and simulation data fusion. npj Comput. Mater. 10, 2024, DOI: 10.1038/s41524-024-01251-4
Lehre
- Lecturer: Hands-On Deep Learning Praktikum, WiSe 2022/23
- Lecturer: Physics of Fluids, WiSe 2023/24
- TA: Physics-Informed Machine Learning, SoSe 2023
- Lecturer: Hands-On Deep Learning Praktikum, WiSe 2022/23
- TA: Physics of Fluids, WiSe 2022/23
- TA: Physics-Informed Machine Learning, SoSe 2022
- TA: Physics-Informed Machine Learning, SoSe 2021