Tristan Bereau
Affiliations: | Max Planck Institute for Polymer Research Mainz |
Google:
"Tristan Bereau"Parents
Sign in to add mentor| Markus Deserno | grad student | Carnegie Mellon (Physics Tree) | |
| Markus Meuwly | post-doc | University of Basel |
BETA: Related publications
See more...
Publications
|
You can help our author matching system! If you notice any publications incorrectly attributed to this author, please sign in and mark matches as correct or incorrect. |
| Bereau T, Walter LJ, Rudzinski JF. (2024) Martignac: Computational Workflows for Reproducible, Traceable, and Composable Coarse-Grained Martini Simulations. Journal of Chemical Information and Modeling |
| Scheffler M, Aeschlimann M, Albrecht M, et al. (2022) FAIR data enabling new horizons for materials research. Nature. 604: 635-642 |
| Mondal P, Cazade PA, Das AK, et al. (2021) Multipolar Force Fields for Amide-I Spectroscopy from Conformational Dynamics of the Alanine Trimer. The Journal of Physical Chemistry. B |
| Rudzinski JF, Kloth S, Wörner S, et al. (2021) Dynamical properties across different coarse-grained models for ionic liquids. Journal of Physics. Condensed Matter : An Institute of Physics Journal |
| Rudzinski JF, Bereau T. (2020) Coarse-grained conformational surface hopping: Methodology and transferability. The Journal of Chemical Physics. 153: 214110 |
| Liu C, Brandenburg JG, Valsson O, et al. (2020) Free-energy landscape of polymer-crystal polymorphism. Soft Matter |
| Girard M, Bereau T. (2020) Regulating Lipid Composition Rationalizes Acyl Tail Saturation Homeostasis in Ectotherms. Biophysical Journal |
| Rauer C, Bereau T. (2020) Hydration free energies from kernel-based machine learning: Compound-database bias. The Journal of Chemical Physics. 153: 014101 |
| Scherer C, Scheid R, Andrienko D, et al. (2020) Kernel-Based Machine Learning for Efficient Simulations of Molecular Liquids. Journal of Chemical Theory and Computation |
| Centi A, Dutta A, Parekh SH, et al. (2020) Inserting Small Molecules across Membrane Mixtures: Insight from the Potential of Mean Force. Biophysical Journal |