4 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…
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 Augmented Jump Chain -- a sparse representation of time-dependent Markov jump processes
Alexander Sikorski, Marcus Weber, Christof Schütte
Modern methods of simulating molecular systems are based on the mathematical theory of Markov operators with a focus on autonomous equilibrated systems. However, non-autonomous phy…