  Hydrous and nominally anhydrous minerals (NAMs) are a fundamental class of
solids of enormous significance to geophysics. They are the water carriers in
the deep geological water cycle and impact structural, elastic, plastic, and
thermodynamic properties and phase relations in Earth's forming aggregates
(rocks). They play a critical role in the geochemical and geophysical processes
that shape the planet. Their complexity has prevented predictive calculations
of their properties, but progress in materials simulations ushered by machine
learning potentials is transforming this state of affairs. Here, we adopt a
hybrid approach that combines deep learning potentials (DP) with the SCAN
meta-GGA functional to simulate a prototypical hydrous system. We illustrate
the success of this approach to simulate $\delta$-AlOOH ($\delta$), a phase
capable of transporting water down to near the core-mantle boundary of the
Earth (~2,900 km depth and ~135 GPa) in subducting slabs. A high-throughput
sampling of phase space using molecular dynamics simulations with DP-potentials
sheds light on the hydrogen-bond behavior and proton diffusion at geophysical
conditions. These simulations provide a pathway for a deeper understanding of
these crucial components that shape Earth's internal state.
