Hydrogen diffusion in garnet: insights from atomistic simulations

TL;DR

This study uses advanced atomistic simulations with machine learning to investigate hydrogen diffusion in garnet, revealing how defects and atomic vibrations influence hydrogen mobility and explaining experimental diffusion behaviors.

Contribution

It introduces a machine learning-based approach to simulate hydrogen diffusion in garnet, highlighting the impact of vacancies and defects on diffusivity and activation energies.

Findings

Hydrogen diffuses rapidly in defect-free garnet lattices.

Defects significantly reduce hydrogen diffusivity.

Two diffusion regimes are identified based on hydrogen content.

Abstract

Garnet has been widely used to decipher the pressure-temperature-time history of rocks, but its physical properties such as elasticity and diffusion are strongly affected by trace amounts of hydrogen. Experimental measurements of H diffusion in garnet are limited to room pressure. We use atomistic simulations to study H diffusion in perfect and defective garnet lattices, focusing on protonation defects at the Si and Mg sites, which are shown to be energetically favored. The ab-initio simulation of H diffusion is computationally challenging due to a transient trapping of H, which is overcome with machine learning techniques by training a deep neural network that encodes the interatomic potential. Our results show high mobility of hydrogen in defect free garnet lattices, whereas H diffusivity is significantly diminished in defective lattices. Tracer simulation focusing on H alone highlights the vital role of atomic vibrations of heavier atoms like Mg in the untrapping of H atoms. Two regimes of H diffusion are identified: a diffuser-dominated regime at high hydrogen content with low activation energies, due to saturation of vacancies by hydrogen, and a vacancy-dominated regime at low hydrogen content with high activation energies, due to trapping of H atoms at vacancy sites. These regimes account for experimental observations, such as a H-concentration dependent diffusivity and the discrepancy in activation energy between deprotonation and D-H exchange experiments. This study underpins the crucial role of vacancies in H diffusion and demonstrates the utility of machine-learned interatomic potentials in studying kinetic processes in the Earth's interior.