Learning molecular dynamics with simple language model built upon long short-term memory neural network.

ABSTRACT: Recurrent neural networks have led to breakthroughs in natural language processing and speech recognition. Here we show that recurrent networks, specifically long short-term memory networks can also capture the temporal evolution of chemical/biophysical trajectories. Our character-level language model learns a probabilistic model of 1-dimensional stochastic trajectories generated from higher-dimensional dynamics. The model captures Boltzmann statistics and also reproduces kinetics across a spectrum of timescales. We demonstrate how training the long short-term memory network is equivalent to learning a path entropy, and that its embedding layer, instead of representing contextual meaning of characters, here exhibits a nontrivial connectivity between different metastable states in the under

SUBMITTER: Tsai ST 

PROVIDER: S-EPMC7547727 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature