Frank Noé
Affiliations: | Department of Mathematics and Computer Science | Freie Universität Berlin, Berlin, Germany |
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Sign in to add trainee| Johannes Schöneberg | grad student | 2008-2014 | |
| Brooke Husic | post-doc | ||
| Jun Zhang | post-doc | 2017- | FU Berlin |
| Jan Hermann | post-doc | 2019-2020 | FU Berlin |
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Publications
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| Majewski M, Pérez A, Thölke P, et al. (2023) Machine learning coarse-grained potentials of protein thermodynamics. Nature Communications. 14: 5739 |
| Hermann J, Spencer J, Choo K, et al. (2023) Ab initio quantum chemistry with neural-network wavefunctions. Nature Reviews. Chemistry |
| Mardt A, Hempel T, Clementi C, et al. (2022) Deep learning to decompose macromolecules into independent Markovian domains. Nature Communications. 13: 7101 |
| Hempel T, Olsson S, Noé F. (2022) Markov field models: Scaling molecular kinetics approaches to large molecular machines. Current Opinion in Structural Biology. 77: 102458 |
| Mardt A, Noé F. (2021) Progress in deep Markov state modeling: Coarse graining and experimental data restraints. The Journal of Chemical Physics. 155: 214106 |
| Del Razo MJ, Dibak M, Schütte C, et al. (2021) Multiscale molecular kinetics by coupling Markov state models and reaction-diffusion dynamics. The Journal of Chemical Physics. 155: 124109 |
| Chen Y, Krämer A, Charron NE, et al. (2021) Machine learning implicit solvation for molecular dynamics. The Journal of Chemical Physics. 155: 084101 |
| Hempel T, Del Razo MJ, Lee CT, et al. (2021) Independent Markov decomposition: Toward modeling kinetics of biomolecular complexes. Proceedings of the National Academy of Sciences of the United States of America. 118 |
| Glielmo A, Husic BE, Rodriguez A, et al. (2021) Unsupervised Learning Methods for Molecular Simulation Data. Chemical Reviews |
| Wang J, Charron N, Husic B, et al. (2021) Multi-body effects in a coarse-grained protein force field. The Journal of Chemical Physics. 154: 164113 |