ISOKANN learns collective variables via neural Koopman subspaces and derives effective dynamics to compute transition rates, times, and pathways from molecular simulation data.
On finding optimal collective variables for complex sys- tems by minimizing the deviation between effective and full dynamics.Multiscale Modeling & Simulation, 23(2):924–958
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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.