ISOKANN learns collective variables via neural Koopman subspaces and derives effective dynamics to compute transition rates, times, and pathways from molecular simulation data.
Characterization of transition states in conformational dynamics using fuzzy sets
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Effective Dynamics and Transition Pathways from Koopman-Inspired Neural Learning of Collective Variables
ISOKANN learns collective variables via neural Koopman subspaces and derives effective dynamics to compute transition rates, times, and pathways from molecular simulation data.