| Comparison of iterative inverse coarse-graining methods D Rosenberger, M Hanke, NFA van der Vegt The European Physical Journal Special Topics 225, 1323-1345, 2016 | 49 | 2016 |
| Addressing the temperature transferability of structure based coarse graining models D Rosenberger, NFA van der Vegt Physical Chemistry Chemical Physics 20 (9), 6617-6628, 2018 | 45 | 2018 |
| Modeling of peptides with classical and novel machine learning force fields: A comparison D Rosenberger, JS Smith, AE Garcia The Journal of Physical Chemistry B 125 (14), 3598-3612, 2021 | 42 | 2021 |
| Transferability of local density-assisted implicit solvation models for homogeneous fluid mixtures D Rosenberger, T Sanyal, MS Shell, NFA van der Vegt Journal of Chemical Theory and Computation 15 (5), 2881-2895, 2019 | 25 | 2019 |
| Machine learning of consistent thermodynamic models using automatic differentiation D Rosenberger, K Barros, TC Germann, N Lubbers Physical Review E 105 (4), 045301, 2022 | 20 | 2022 |
| Multiscale simulation of plasma flows using active learning A Diaw, K Barros, J Haack, C Junghans, B Keenan, YW Li, D Livescu, ... Physical Review E 102 (2), 023310, 2020 | 20 | 2020 |
| Slicing and dicing: Optimal coarse-grained representation to preserve molecular kinetics W Yang, C Templeton, D Rosenberger, A Bittracher, F Nüske, F Noé, ... ACS Central Science 9 (2), 186-196, 2023 | 18 | 2023 |
| Phase equilibria modeling with systematically coarse-grained models—A comparative study on state point transferability G Deichmann, M Dallavalle, D Rosenberger, NFA van der Vegt The Journal of Physical Chemistry B 123 (2), 504-515, 2018 | 18 | 2018 |
| Evaluating diffusion and the thermodynamic factor for binary ionic mixtures D Rosenberger, N Lubbers, TC Germann Physics of Plasmas 27 (10), 2020 | 16 | 2020 |
| Relative entropy indicates an ideal concentration for structure-based coarse graining of binary mixtures D Rosenberger, NFA van der Vegt Physical Review E 99 (5), 053308, 2019 | 5 | 2019 |
| Peering inside the black box: Learning the relevance of many-body functions in Neural Network potentials K Bonneau, J Lederer, C Templeton, D Rosenberger, KR Müller, ... arXiv preprint arXiv:2407.04526, 2024 | 3 | 2024 |
| VOTCA: multiscale frameworks for quantum and classical simulations in soft matter B Baumeier, J Wehner, N Renaud, FZ Ruiz, R Halver, P Madhikar, ... Journal of Open Source Software 9 (LA-UR-24-25313), 2024 | 1 | 2024 |
| Biradicals based on PROXYL Containing Building Blocks for Efficient Dynamic Nuclear Polarization in Biotolerant Media K Herr, MV Höfler, H Heise, F Aussenac, F Kornemann, D Rosenberger, ... Journal of Magnetic Resonance Open, 100152, 2024 | | 2024 |
| Dynamic framework for large-scale modeling of membranes and peripheral proteins. M Sadeghi, D Rosenberger Methods in Enzymology 701, 457-514, 2024 | | 2024 |
| T cell interaction partners of DHHC20 D Haxhiraj, B Kuropka, E Brencher, D Rosenberger, H Brandt, D Speck, ... bioRxiv, 2024.02. 14.580290, 2024 | | 2024 |
| Bottom-up coarse-graining scheme for the preservation of relevant slow degrees of freedom in soft materials. D Rosenberger, F Noe, C Clementi, A Bittracher, C Templeton, W Yang, ... APS March Meeting Abstracts 2023, S17. 007, 2023 | | 2023 |
| Multiscale Simulation of Plasma Flows Using Active Learning J Haack, A Diaw, K Barros, C Junghans, B Keenan, YW Li, D Livescu, ... APS Division of Plasma Physics Meeting Abstracts 2020, NO05. 011, 2020 | | 2020 |
| From the bottom up-A systematic study of structure based coarse graining approaches D Rosenberger TUprints, 2019 | | 2019 |
