  Exascale computing holds great opportunities for molecular dynamics (MD)
simulations. However, to take full advantage of the new possibilities, we must
learn how to focus computational power on the discovery of complex molecular
mechanisms, and how to extract them from enormous amounts of data. Both aspects
still rely heavily on human experts, which becomes a serious bottleneck when a
large number of parallel simulations have to be orchestrated to take full
advantage of the available computing power. Here, we use artificial
intelligence (AI) both to guide the sampling and to extract the relevant
mechanistic information. We combine advanced sampling schemes with statistical
inference, artificial neural networks, and deep learning to discover molecular
mechanisms from MD simulations. Our framework adaptively and autonomously
initializes simulations and learns the sampled mechanism, and is thus suitable
for massively parallel computing architectures. We propose practical solutions
to make the neural networks interpretable, as illustrated in applications to
molecular systems.
