925 citations · 3.1k across the 34 of their papers we have counts for
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Machine learning for protein folding and dynamics
Frank Noé, Gianni De Fabritiis, Cecilia Clementi
Many aspects of the study of protein folding and dynamics have been affected by the recent advances in machine learning. Methods for the prediction of protein structures from their…
Machine learning for molecular simulation
Frank Noé, Alexandre Tkatchenko, Klaus-Robert Müller +1
Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for a machine learning re…
Data-driven approximation of the Koopman generator: Model reduction, system identification, and control
Stefan Klus, Feliks Nüske, Sebastian Peitz +3
We derive a data-driven method for the approximation of the Koopman generator called gEDMD, which can be regarded as a straightforward extension of EDMD (extended dynamic mode deco…
Tensor-based computation of metastable and coherent sets
Feliks Nüske, Patrick Gelß, Stefan Klus +1
Recent years have seen rapid advances in the data-driven analysis of dynamical systems based on Koopman operator theory and related approaches. On the other hand, low-rank tensor p…
Extensible and Scalable Adaptive Sampling on Supercomputers
Eugen Hruska, Vivekanandan Balasubramanian, Hyungro Lee +2
The accurate sampling of protein dynamics is an ongoing challenge despite the utilization of High-Performance Computers (HPC) systems. Utilizing only "brute force" MD simulations r…
Coarse-graining Molecular Systems by Spectral Matching
Feliks Nüske, Lorenzo Boninsegna, Cecilia Clementi
Coarse-graining has become an area of tremendous importance within many different research fields. For molecular simulation, coarse-graining bears the promise of finding simplified…