Probing the state of hydrogen in δ − AlOOH at mantle conditions with machine learning potential

Luo, Chenxing; Sun, Yang; Wentzcovitch, Renata M.

doi:10.1103/PhysRevResearch.6.013292

Probing the state of hydrogen in $δ − AlOOH$ at mantle conditions with machine learning potential

Journal Article · Mon Mar 18 00:00:00 EDT 2024 · Physical Review Research

DOI:https://doi.org/10.1103/PhysRevResearch.6.013292· OSTI ID:2325201

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Hydrous and nominally anhydrous minerals 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 (DPs) with the strongly constrained and appropriately normed meta-generalized gradient approximation functional to simulate a prototypical hydrous system. We illustrate the success of this approach to simulate $δ − AlOOH$ $(δ)$ , a phase capable of transporting water down to near the core-mantle boundary of the Earth ( $\sim 2900 km$ depth and $\sim 135 GPa$ ) in subducting slabs. A high-throughput sampling of phase space using molecular dynamics simulations with DPs 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.

Published by the American Physical Society 2024

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Sponsoring Organization:: USDOE

Grant/Contract Number:: SC0019759

OSTI ID:: 2325201

Journal Information:: Physical Review Research, Journal Name: Physical Review Research Journal Issue: 1 Vol. 6; ISSN PPRHAI; ISSN 2643-1564

Publisher:: American Physical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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Probing the state of hydrogen in δ − AlOOH at mantle conditions with machine learning potential

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Probing the state of hydrogen in $δ − AlOOH$ at mantle conditions with machine learning potential