ISOKANN learns collective variables via neural Koopman subspaces and derives effective dynamics to compute transition rates, times, and pathways from molecular simulation data.
Identification of slow molecular order parameters for markov model construction.The Journal of Chemical Physics, 139(1):015102, 2013
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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.