Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics.
Christoph WehmeyerFrank NoéPublished in: The Journal of chemical physics (2018)
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.