Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics

Christoph Wehmeyer, Frank Noé
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