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Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
2018
Journal of Chemical Physics
Inspired by the success of deep learning techniques in the physical and chemical sciences, we apply a modification of an autoencoder type deep neural network to the task of dimension reduction of molecular dynamics data. We can show that our time-lagged autoencoder reliably finds low-dimensional embeddings for high-dimensional feature spaces which capture the slow dynamics of the underlying stochastic processes - beyond the capabilities of linear dimension reduction techniques.
doi:10.1063/1.5011399
pmid:29960344
fatcat:4lthaotfqfblfkh5uyaxmpgwza