  Recurrent neural networks (RNNs) have led to breakthroughs in natural
language processing and speech recognition, wherein hundreds of millions of
people use such tools on a daily basis through smartphones, email servers and
other avenues. In this work, we show such RNNs, specifically Long Short-Term
Memory (LSTM) neural networks can also be applied to capturing the temporal
evolution of typical trajectories arising in chemical and biological physics.
Specifically, we use a character-level language model based on LSTM. This
learns a probabilistic model from 1-dimensional stochastic trajectories
generated from molecular dynamics simulations of a higher dimensional system.
We show that the model can not only capture the Boltzmann statistics of the
system but it also reproduce kinetics at a large spectrum of timescales. We
demonstrate how the embedding layer, introduced originally for representing the
contextual meaning of words or characters, exhibits here a nontrivial
connectivity between different metastable states in the underlying physical
system. We demonstrate the reliability of our model and interpretations through
different benchmark systems and a single molecule force spectroscopy trajectory
for multi-state riboswitch. We anticipate that our work represents a stepping
stone in the understanding and use of RNNs for modeling and predicting dynamics
of complex stochastic molecular systems.
