Hydrogen Diffusion in Garnet: Insights From Atomistic Simulations

IF 2.9 2区 地球科学 Q2 GEOCHEMISTRY & GEOPHYSICS
Xin Zhong, Felix Höfling, Timm John
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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. Transient trapping of H renders ab-initio simulations of H diffusion computationally challenging, which is overcome with machine learning techniques by training a deep neural network that encodes the interatomic potential. Our results from such deep potential molecular dynamics (DeePMD) simulations show high mobility of hydrogen in defect-free garnet lattices, whereas H diffusivity is significantly diminished in defective lattices. Tracer simulations focusing on H alone highlight the vital role of atomic vibrations of heavier atoms like Mg on the release 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.

Abstract Image

氢在石榴石中的扩散:来自原子模拟的见解
石榴石已被广泛用于破译岩石的压力-温度-时间历史,但其物理性质,如弹性和扩散受到微量氢的强烈影响。氢在石榴石中扩散的实验测量仅限于室温压力。我们使用原子模拟研究了H在完美和缺陷石榴石晶格中的扩散,重点研究了Si和Mg位的质子化缺陷,这些缺陷被证明是能量有利的。氢的瞬态捕获使得氢扩散的从头模拟在计算上具有挑战性,这是通过训练一个编码原子间势的深度神经网络的机器学习技术来克服的。我们的深势分子动力学(DeePMD)模拟结果显示,氢在无缺陷的石榴石晶格中具有高迁移率,而氢的扩散率在缺陷晶格中显著降低。仅关注氢原子的示踪模拟强调了像Mg这样的较重原子的原子振动对氢原子释放的重要作用。确定了两种H扩散模式:在高氢含量下,由于氢饱和导致的低活化能的扩散体主导模式;在低氢含量下,由于H原子在空位位置被捕获而具有高活化能的空位主导模式。这些机制解释了实验观察,如h浓度依赖的扩散率和去质子化和D-H交换实验之间活化能的差异。这项研究支持了空位在氢扩散中的关键作用,并证明了机器学习原子间势在研究地球内部动力学过程中的效用。
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来源期刊
Geochemistry Geophysics Geosystems
Geochemistry Geophysics Geosystems 地学-地球化学与地球物理
CiteScore
5.90
自引率
11.40%
发文量
252
审稿时长
1 months
期刊介绍: Geochemistry, Geophysics, Geosystems (G3) publishes research papers on Earth and planetary processes with a focus on understanding the Earth as a system. Observational, experimental, and theoretical investigations of the solid Earth, hydrosphere, atmosphere, biosphere, and solar system at all spatial and temporal scales are welcome. Articles should be of broad interest, and interdisciplinary approaches are encouraged. Areas of interest for this peer-reviewed journal include, but are not limited to: The physics and chemistry of the Earth, including its structure, composition, physical properties, dynamics, and evolution Principles and applications of geochemical proxies to studies of Earth history The physical properties, composition, and temporal evolution of the Earth''s major reservoirs and the coupling between them The dynamics of geochemical and biogeochemical cycles at all spatial and temporal scales Physical and cosmochemical constraints on the composition, origin, and evolution of the Earth and other terrestrial planets The chemistry and physics of solar system materials that are relevant to the formation, evolution, and current state of the Earth and the planets Advances in modeling, observation, and experimentation that are of widespread interest in the geosciences.
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