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Machine Learning Coarse-Grained Potentials of Protein Thermodynamics


Dec 14, 2022
Maciej Majewski, Adrià Pérez, Philipp Thölke, Stefan Doerr, Nicholas E. Charron, Toni Giorgino, Brooke E. Husic, Cecilia Clementi, Frank Noé, Gianni De Fabritiis

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Deeptime: a Python library for machine learning dynamical models from time series data


Oct 28, 2021
Moritz Hoffmann, Martin Scherer, Tim Hempel, Andreas Mardt, Brian de Silva, Brooke E. Husic, Stefan Klus, Hao Wu, Nathan Kutz, Steven L. Brunton, Frank Noé

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Machine Learning Implicit Solvation for Molecular Dynamics


Jun 14, 2021
Yaoyi Chen, Andreas Krämer, Nicholas E. Charron, Brooke E. Husic, Cecilia Clementi, Frank Noé

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* 15 pages, 6 figures 

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Coarse Graining Molecular Dynamics with Graph Neural Networks


Aug 21, 2020
Brooke E. Husic, Nicholas E. Charron, Dominik Lemm, Jiang Wang, Adrià Pérez, Andreas Krämer, Yaoyi Chen, Simon Olsson, Gianni de Fabritiis, Frank Noé, Cecilia Clementi

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* 27 pages, 23 figures 

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Kernel canonical correlation analysis approximates operators for the detection of coherent structures in dynamical data


Apr 16, 2019
Stefan Klus, Brooke E. Husic, Mattes Mollenhauer

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Variational Selection of Features for Molecular Kinetics


Nov 28, 2018
Martin K. Scherer, Brooke E. Husic, Moritz Hoffmann, Fabian Paul, Hao Wu, Frank Noé

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* 12 pages, 10 figures 

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PotentialNet for Molecular Property Prediction


Oct 22, 2018
Evan N. Feinberg, Debnil Sur, Zhenqin Wu, Brooke E. Husic, Huanghao Mai, Yang Li, Saisai Sun, Jianyi Yang, Bharath Ramsundar, Vijay S. Pande

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* 13 pages, 5 figures, 8 tables 

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Simultaneous Coherent Structure Coloring facilitates interpretable clustering of scientific data by amplifying dissimilarity


Sep 03, 2018
Brooke E. Husic, Kristy L. Schlueter-Kuck, John O. Dabiri

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* In revision 

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Unsupervised learning of dynamical and molecular similarity using variance minimization


Dec 20, 2017
Brooke E. Husic, Vijay S. Pande

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* NIPS 2017 Workshop on Machine Learning for Molecules and Materials 

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