Browse by Authors

2023

Charron, Nicholas E. and Musil, Felix and Guljas, Andrea and Chen, Yaoyi and Bonneau, Klara and Pasos-Trejo, Aldo S. and Venturin, Jacopo and Gusew, Daria and Zaporozhets, Iryna and Krämer, Andreas and Templeton, Clark and Kelkar, Atharva and Durumeric, Alexander E.P. and Olsson, Simon and Pérez, Adrià and Majewski, Maciej and Husic, Brooke E. and Patel, Ankit and Fabritiis, Gianni De and Noé, Frank and Clementi, Cecilia (2023) Navigating protein landscapes with a machine-learned transferable coarse-grained model. Preprint . (Unpublished)

Majewski, Maciej and Pérez, Adrià and Thölke, Philipp and Doerr, Stefan and Charron, Nicholas E. and Giorgino, Toni and Husic, Brooke E. and Clementi, Cecilia and Noé, Frank and De Fabritiis, Gianni (2023) Machine Learning Coarse-Grained Potentials of Protein Thermodynamics. Nature Communications, 14 .

2021

Hoffmann, Moritz and Scherer, Martin and Hempel, Tim and Mardt, Andreas and de Silva, Brian and Husic, Brooke E. and Klus, Stefan and Wu, Hao and Kutz, Nathan and Brunton, Steven L (2021) Deeptime: a Python library for machine learning dynamical models from time series data. Mach. Learn.: Sci. Technol. 3 (2022), 3 (015009). pp. 1-28.

Chen, Yaoyi and Krämer, Andreas and Charron, Nicholas E. and Husic, Brooke E. and Clementi, Cecilia and Noé, Frank (2021) Machine learning implicit solvation for molecular dynamics. The Journal of Chemical Physics, 155 (084101). pp. 1-15.

2020

Husic, Brooke E. and Charron, Nicholas E. and Lemm, Dominik and Wang, Jiang and Pérez, Adrià and Majewski, Maciej and Krämer, Andreas and Chen, Yaoyi and Olsson, Simon and de Fabritiis, Gianni and Noé, Frank and Clementi, Cecilia (2020) Coarse graining molecular dynamics with graph neural networks. J. Chem. Phys., 153 (194101). pp. 1-17.

2019

Klus, Stefan and Husic, Brooke E. and Mollenhauer, Mattes and Noé, Frank (2019) Kernel methods for detecting coherent structures in dynamical data. Chaos, 29 (12).