Hydrogen diffusion in garnet: insights from atomistic simulations
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arXiv
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| Natura: | Preprint |
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2024
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| _version_ | 1866909492831059968 |
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| author | Zhong, Xin Höfling, Felix John, Timm |
| author_facet | Zhong, Xin Höfling, Felix John, Timm |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_07724 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hydrogen diffusion in garnet: insights from atomistic simulations Zhong, Xin Höfling, Felix John, Timm Materials Science Soft Condensed Matter Geophysics 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. |
| title | Hydrogen diffusion in garnet: insights from atomistic simulations |
| topic | Materials Science Soft Condensed Matter Geophysics |
| url | https://arxiv.org/abs/2410.07724 |