Brooke Husic
Affiliations: | Freie Universität Berlin, Berlin, Germany |
Area:
chemistry, biophysicsGoogle:
"Brooke Husic"Mean distance: (not calculated yet)
Parents
Sign in to add mentor| Jay W. Ponder | research assistant | (Physics Tree) | |
| Vijay S. Pande | grad student | ||
| David J. Wales | grad student | ||
| Frank Noé | post-doc |
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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 |
| Chen Y, Krämer A, Charron NE, et al. (2021) Machine learning implicit solvation for molecular dynamics. The Journal of Chemical Physics. 155: 084101 |
| 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 |
| Husic BE, Charron NE, Lemm D, et al. (2020) Coarse graining molecular dynamics with graph neural networks. The Journal of Chemical Physics. 153: 194101 |
| Klus S, Husic BE, Mollenhauer M, et al. (2019) Kernel methods for detecting coherent structures in dynamical data. Chaos (Woodbury, N.Y.). 29: 123112 |
| Scherer MK, Husic BE, Hoffmann M, et al. (2019) Variational selection of features for molecular kinetics. The Journal of Chemical Physics. 150: 194108 |
| Husic BE, Noé F. (2019) Deflation reveals dynamical structure in nondominant reaction coordinates The Journal of Chemical Physics. 151: 054103 |
| Röder K, Joseph JA, Husic BE, et al. (2019) Energy Landscapes for Proteins: From Single Funnels to Multifunctional Systems Advanced Theory and Simulations. 2: 1800175 |
| Feinberg EN, Sur D, Wu Z, et al. (2018) PotentialNet for Molecular Property Prediction. Acs Central Science. 4: 1520-1530 |