We employ the recently developed variational approach for Markov processes (VAMP) to develop a deep learning framework for molecular kinetics using neural networks, dubbed VAMPnets. A VAMPnet encodes the entire mapping from molecular coordinates to Markov states and learns optimal feature transformations, non-linear dimension reduction, cluster discretization and MSM estimation within a single end-to-end learning framework.
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