Probing the state of hydrogen in $\delta$-AlOOH at mantle conditions with machine learning potential

TL;DR

This paper demonstrates how machine learning potentials can accurately simulate the behavior of hydrous minerals like δ-AlOOH under Earth's mantle conditions, revealing insights into hydrogen bonding and proton diffusion at high pressures.

Contribution

It introduces a hybrid deep learning potential approach combined with the SCAN functional to simulate complex hydrous minerals at geophysical conditions, enabling predictive insights.

Findings

Hydrogen-bond behavior at mantle conditions elucidated

Proton diffusion pathways characterized at high pressure

Machine learning potentials enable accurate geophysical mineral simulations

Abstract

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.