1 citations · 1 across the 6 of their papers we have counts for
6 papers
Effective Dynamics and Transition Pathways from Koopman-Inspired Neural Learning of Collective Variables
Alexander Sikorski, Luca Donati, Marcus Weber +1
The ISOKANN (Invariant Subspaces of Koopman Operators Learned by Artificial Neural Networks) framework provides a data-driven route to extract collective variables (CVs) and effect…
On-the-Fly Lifting of Coarse Reaction-Coordinate Paths to Full-Dimensional Transition Path Ensembles
Christof Schütte, Alexander Sikorski, Jakob Kresse +1
Effective dynamics on a low-dimensional collective-variable (CV) or latent space can be simulated far more cheaply than the underlying high-dimensional stochastic system, but explo…
Importance sampling of unbounded random stopping times: computing committor functions and exit rates without reweighting
Carsten Hartmann, Annika Jöster, Christof Schütte +2
Rare events in molecular dynamics are often related to noise-induced transitions between different macroscopic states (e.g., in protein folding). A common feature of these rare tra…
Revealing the Atomistic Mechanism of Rare Events in Molecular Dynamics
Jakob J. Kresse, Alexander Sikorski, Marcus Weber
Interpretable reaction coordinates are essential for understanding rare conformational transitions in molecular dynamics. The Atomistic Mechanism Of Rare Events in Molecular Dynami…
The Kramers turnover in terms of a macro-state projection on phase space
Luca Donati, Christof Schutte, Marcus Weber
We have investigated how Langevin dynamics is affected by the friction coefficient using the novel algorithm ISOKANN, which combines the transfer operator approach with modern mach…
Tensor-SqRA: Modeling the Transition Rates of Interacting Molecular Systems in terms of Potential Energies
Alexander Sikorski, Amir Niknejad, Marcus Weber +1
Estimating the rate of rare conformational changes in molecular systems is one of the goals of Molecular Dynamics simulations. In the past decades, a lot of progress has been done…