3 years ago

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

Christoph Wehmeyer, Frank Noé

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.

Publisher URL: http://arxiv.org/abs/1710.11239

DOI: arXiv:1710.11239v1

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