Hydrogen Diffusion in the Lower Mantle Revealed by Machine Learning Potentials

Abstract

Hydrogen may be incorporated into nominally anhydrous minerals including bridgmanite and post‐perovskite as defects, making the Earth's deep mantle a potentially significant water reservoir. The diffusion of hydrogen and its contribution to the electrical conductivity in the lower mantle are rarely explored and remain largely unconstrained. Here we calculate hydrogen diffusivity in hydrous bridgmanite and post‐perovskite, using molecular dynamics simulations driven by machine learning potentials of ab initio quality. Our findings reveal that hydrogen diffusivity significantly increases with increasing temperature and decreasing pressure, and is considerably sensitive to hydrogen incorporation mechanism. Among the four defect mechanisms examined, (Mg + 2H)Si and (Al + H)Si show similar patterns and yield the highest hydrogen diffusivity. Hydrogen diffusion is generally faster in post‐perovskite than in bridgmanite, and these two phases exhibit distinct diffusion anisotropies. Overall, hydrogen diffusion is slow on geological time scales and may result in heterogeneous water distribution in the lower mantle. Additionally, the proton conductivity of bridgmanite for (Mg + 2H)Si and (Al + H)Si defects aligns with the same order of magnitude of lower mantle conductivity, suggesting that the water distribution in the lower mantle may be inferred by examining the heterogeneity of electrical conductivity.

Grain boundary diffusion cannot explain the W isotope heterogeneities of the deep mantle

“…S7), and distribution of elements and vacancies (Fig. S8), to ensure that diffusivity results are converged and robust 24,29 . Fig.…”

“…Recently, we developed an MLP for Mg-O-Fe system covering a wide compositional range of various Mg:O:Fe ratios at pressure up to 200 GPa and temperature up to 8000 K 66 . Here we further extend the compositional space of this MLP to include one more element X (X is either Pt or W) following the same procedure outlined at Peng & Deng 29 . Two MLPs are built with training sets inherited from the Mg-O-Fe MLP 66 and additional ones that entail Mg-O-Fe-X mixtures.…”