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 (Unpublished)

Arts, Marloes and Garcia Sattorras, Victor and Huang, Chin-Wei and Zuegner, Daniel and Federici, Marco and Clementi, Cecilia and Noé, Frank and Pinsler, Robert and van den Berg, Rianne (2023) Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics. JCTC - Journal of Chemical Theory and Computation, 19 . pp. 6151-6159.

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 .

Krämer, Andreas and Durumeric, Alexander E.P. and Charron, Nicholas E. and Chen, Yaoyi and Clementi, Cecilia and Noé, Frank (2023) Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics. The Journal of Physical Chemistry, 14 . pp. 3970-3979.

Durumeric, Alexander E.P. and Charron, Nicholas E. and Templeton, Clark and Musil, Félix and Bonneau, Klara and Pasos-Trejo, Aldo S. and Chen, Yaoyi and Kelkar, Atharva and Noé, Frank and Clementi, Cecilia (2023) Machine learned coarse-grained protein force-fields: Are we there yet? Current Opinion in Structural Biology, 79 .

Köhler, Jonas and Chen, Yaoyi and Krämer, Andreas and Clementi, Cecilia and Noé, Frank (2023) Flow-Matching: Efficient Coarse-Graining of Molecular Dynamics without Forces. J. Chem. Theory Comput., 19 (3). pp. 942-952.

Yang, Wangfei and Templeton, Clark and Rosenberger, David and Bittracher, Andreas and Nüske, Feliks and Noé, Frank and Clementi, Cecilia (2023) Slicing and Dicing: Optimal Coarse-Grained Representation to Preserve Molecular Kinetics. ACS Cent. Sci., 9 (2). pp. 186-196.

2022

Mardt, Andreas and Hempel, Tim and Clementi, Cecilia and Noé, Frank (2022) Deep learning to decompose macromolecules into independent Markovian domains. nature communications, 13 .

Musil, Félix and Zaporozhets, Iryna and Noé, Frank and Clementi, Cecilia and Kapil, Venkat (2022) Quantum dynamics using path integral coarse-graining. The Journal of Chemical Physics, 157 (18).

2021

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.

Nüske, Feliks and Koltai, Péter and Boninsegna, Lorenzo and Clementi, Cecilia (2021) Spectral Properties of Effective Dynamics from Conditional Expectations. Entropy, 23 (134). pp. 1-25.

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.

Noé, Frank and Tkatchenko, Alexandre and Müller, Klaus-Robert and Clementi, Cecilia (2020) Machine Learning for Molecular Simulation. Annual Review of Physical Chemistry, 71 . pp. 361-390.

Klus, Stefan and Nüske, Feliks and Peitz, Sebastian and Niemann, Jan-Hendrik and Clementi, Cecilia and Schütte, Christof (2020) Data-driven approximation of the Koopman generator: Model reduction, system identification, and control. Physica D: Nonlinear Phenomena, 406 (132416).

2019

Nüske, Feliks and Boninsegna, Lorenzo and Clementi, Cecilia (2019) Coarse-graining molecular systems by spectral matching. J. Chem. Phys., 151 . pp. 1-10. ISSN 0021-9606, ESSN: 1089-7690