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Deep saline aquifers are one of the best options for large-scale andlong-term hydrogen storage. Predicting the diffusion coefficient of hydrogenmolecules at the conditions of saline aquifers is critical for modellinghydrogen storage. The diffusion coefficient of hydrogen molecules in chloridebrine with different cations ($\mathrm{Na}^+$, $\mathrm{K}^+$,$\mathrm{Ca}^{2+}$) containing up to 5 $\mathrm{mol/kg_{H_2O}}$ concentrationis numerically investigated using molecular dynamics (MD) simulation. A widerange of pressure (1-218 atm) and temperature (298-648 K) conditions is appliedto cover the realistic operational conditions of the aquifers. We find that thetemperature, pressure and properties of ions (compositions and concentrations)affect the hydrogen diffusion coefficient. An Arrhenius behavior of the effectof temperature on the diffusion coefficient has been observed with thetemperature independent parameters fitted using the ion concentration underconstant pressure. However, it is noted that the pressure strongly affects thediffusive behavior of hydrogen at the high temperature ($\geq$ 400 K) regime,indicating the inaccuracy of the Arrhenius model. Hence, we combine theobtained MD results with four models of machine learning (ML), including linearregression (LR), random forest (RF), extra tree (ET) and gradient boosting (GB)to provide effective predictions on the hydrogen diffusion. The resultantcombination of GB model with MD data predicts the diffusion of hydrogen moreeffectively as compared to the Arrhenius model and other ML models. Moreover, a$post hoc$ analysis (feature importance rank) has been performed to extract thecorrelation between physical descriptors and simulation results from ML models.

Machine-Learning-Assisted Investigation of the Diffusion of Hydrogen in Brine by Performing Molecular Dynamics Simulation