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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…