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Poster
Deep Generative Markov State Models
Hao Wu · Andreas Mardt · Luca Pasquali · Frank Noe

Tue Dec 04 02:00 PM -- 04:00 PM (PST) @ Room 517 AB #117

We propose a deep generative Markov State Model (DeepGenMSM) learning framework for inference of metastable dynamical systems and prediction of trajectories. After unsupervised training on time series data, the model contains (i) a probabilistic encoder that maps from high-dimensional configuration space to a small-sized vector indicating the membership to metastable (long-lived) states, (ii) a Markov chain that governs the transitions between metastable states and facilitates analysis of the long-time dynamics, and (iii) a generative part that samples the conditional distribution of configurations in the next time step. The model can be operated in a recursive fashion to generate trajectories to predict the system evolution from a defined starting state and propose new configurations. The DeepGenMSM is demonstrated to provide accurate estimates of the long-time kinetics and generate valid distributions for molecular dynamics (MD) benchmark systems. Remarkably, we show that DeepGenMSMs are able to make long time-steps in molecular configuration space and generate physically realistic structures in regions that were not seen in training data.

Author Information

Hao Wu (Freie Universität Berlin)
Andreas Mardt
Luca Pasquali (Freie Universitat Berlin)
Frank Noe (FU Berlin)

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