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