| VAMPnets for deep learning of molecular kinetics A Mardt, L Pasquali, H Wu, F Noé Nature communications 9 (1), 5, 2018 | 664 | 2018 |
| Deeptime: a Python library for machine learning dynamical models from time series data M Hoffmann, M Scherer, T Hempel, A Mardt, B de Silva, BE Husic, S Klus, ... Machine Learning: Science and Technology 3 (1), 015009, 2021 | 112 | 2021 |
| Deep Generative Markov State Models F Wu, Hao and Mardt, Andreas and Pasquali, Luca and Noe Advances in Neural Information Processing Systems 31, 2018 | 82 | 2018 |
| Deep learning Markov and Koopman models with physical constraints A Mardt, L Pasquali, F Noé, H Wu Mathematical and Scientific Machine Learning, 451-475, 2020 | 42 | 2020 |
| Deep learning to decompose macromolecules into independent Markovian domains FN A Mardt, T Hempel, C Clementi Nature communications 13, 2022 | 15 | 2022 |
| Progress in deep Markov state modeling: Coarse graining and experimental data restraints A Mardt, F Noé The Journal of Chemical Physics 155 (21), 2021 | 15 | 2021 |
| Effect of a U: G mispair on the water around DNA A Mardt, RF Gorriz, F Ferraro, P Ulrich, M Zahran, P Imhof Biophysical Chemistry 283, 106779, 2022 | 4 | 2022 |
| Deep learning of the dynamics of complex systems with its applications to biochemical molecules A Mardt | | 2022 |
