Frank Noé

Department of Mathematics and Computer Science Freie Universität Berlin, Berlin, Germany 
"Frank Noé"
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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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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
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