  The dynamics of water molecules plays a vital role in understanding water. We
combined computer simulation and deep learning to study the dynamics of H-bonds
between water molecules. Based on ab initio molecular dynamics simulations and
a newly defined directed Hydrogen (H-) bond population operator, we studied a
typical dynamic process in bulk water: interchange, in which the H-bond donor
reverses roles with the acceptor. By designing a recurrent neural network-based
model, we have successfully classified the interchange and breakage processes
in water. We have found that the ratio between them is approximately 1:4, and
it hardly depends on temperatures from 280 to 360 K. This work implies that
deep learning has the great potential to help distinguish complex dynamic
processes containing H-bonds in other systems.
