Mapping Still Matters: Coarse-Graining with Machine Learning Potentials
Journal of Chemical Information and Modeling, Vol. 66, pp. 2166-2176
Franz Görlich
| Adresse | Multiscale Modeling of Fluid Materials Maschinenwesen, TUM Boltzmannstr. 15 85748 Garching bei München, Deutschland |
| Büro | Gebäude 1, 2. Stock, Raum 5501.02.138 |
| Telefon | +49 (89) 289 - 55301 |
| f.goerlich@tum.de |
Folge mir auf
Forschung und Interessen
- Biomolecular Modelling
- Machine Learning Potentials
- Chemoinformatics
Ausbildung
| 2025 | M.Sc. Chemical and Bioengineering, ETH Zurich |
| 2024 | Master's thesis, University of Oxford |
| 2022 | B.Eng. Chemical Engineering, Niederrhein University of Applied Sciences |
Publications
3 Publikationen
Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials
Journal of Chemical Theory and Computation, Vol. 22, pp. 219-230
A chemical language model for molecular taste prediction
npj Science of Food, Vol. 9