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

By Christoph Wehmeyer, Frank No'e. 2017

403 citations recorded by the source corpus.

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.

Subfields: Materials Chemistry, Molecular Biology, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

In Molecular Biology

In Statistical and Nonlinear Physics

Citations in the corpus

Deep Generative Markov State Models (2018, neutral): “Most of these methods primarily aim at obtaining a low-dimensional latent space that encodes the long-time behavior of the system, and the decoder takes the role of defining or regularizing the learning problem [30, 8, 14, 19, 23].”
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems (2020, methodology): “Though dynamic mode decomposition [197] is the most common technique for approximating the Koopman operator, many recent approaches have been made to approximate Koopman operator embeddings with deep learning models that outperform existing methods [170, 185, 191, 200, 209, 210, 260, 284, 307].”
Machine learning for molecular simulation (2019, methodology): “Analysis and simulation of MD trajectories has also been affected by ML, for instance for the definition of optimal reaction coordinates [19, 20, 21, 22, 23, 24], the estimate of free energy surfaces [25, 26, 27, 22], the construction of Markov State Models [21, 23, 28], and for enhancing MD samplin”
Data-driven discovery of coordinates and governing equations (2019, neutral): “Several methods have trained NNs to predict dynamics, including a time-lagged autoencoder which takes the state at time t as input data and uses an autoencoder-like structure to predict the state at time t + τ (21).”
Integrating Physics-Based Modeling with Machine Learning: A Survey (2020, methodology): “In molecular kinetics, reduced order modeling frameworks are used for approximating the Koopman operator [171, 262].”
Learning Parameters and Constitutive Relationships with Physics Informed Deep Neural Networks (2018, background): “Recently, data-driven approximations of Koopman operators, including dynamic mode decomposition [26], diﬀusion maps [26], delay co-ordinates [8, 4, 1], and neural networks [27, 23, 15, 14, 17], have gained signiﬁcant attention.”