EF1 – Extracting Dynamical Laws from Complex Data



Quantum Kinetics

Project Heads

Klaus-Robert Müller, Frank Noé

Project Members

Brooke Husic (FU)

Project Duration

01.01.2019 – 31.12.2021

Located at

FU Berlin


We want to pioneer the new field of Quantum Kinetics, i.e., the study of quantum mechanical (QM) processes at long timescales. Simulating QM at long timescales has previously been unfeasible but is now coming into reach due to the convergence of mathematics and machine learning methods that we will develop here.

Project Webpages

Selected Publications

  • S Klus, BE Husic, M Mollenhauer, F Noé: Kernel methods for detecting coherent structures in dynamical data. Chaos 29, 123112 (2020)
  • BE Husic, F Noé: Deflation reveals dynamical structure in nondominant reaction coordinates. J. Chem. Phys. 151, 054103 (2019)
  • MK Scherer, BE Husic, M Hoffmann, F Paul, H Wu, F Noé: Variational selection of features for molecular kinetics. J. Chem. Phys. 150, 194108 (2019)
  • J Hermann, Z Schätzle, F Noé: Deep neural network solution of the electronic Schrödinger equation. arXiv preprint arXiv:1909.08423 (2019)
  • J Wang, S Chmiela, KR Müller, F Noè, C Clementi: Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach. J. Chem. Phys (in press)
  • J Wang, S Olsson, C Wehmeyer, A Pérez, NE Charron, G De Fabritiis, F. Noé, C. Clementi: Machine learning of coarse-grained molecular dynamics force fields. ACS Central Sci. 5, 755-767 (2019)
  • F Noé, A Tkatchenko, KR Müller, C Clementi: Machine learning for molecular simulation. Annu. Rev. Phys. Chem. 71, 361-390 (2020)

Selected Pictures

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