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.
