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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.

The thermal conductivity of composites of carbon nanotubes and polyamide-6,6 has been investigated using reverse non-equilibrium molecular dynamics simulations in a full atomistic resolution. It is found, in line with experiments, that the composites have thermal conductivities, which are only moderately larger than that of pure polyamide. The composite conductivities are orders of magnitude less than what would be expected from naïve additivity arguments. This means that the intrinsic thermal conductivities of isolated nanotubes, which exceed the best-conducting metals, cannot be harnessed for heat transport, when the nanotubes are embedded in a polymer matrix. The main reason is the high interfacial thermal resistance between the nanotubes and the polymer, which was calculated in addition to the total composite thermal conductivity as well as that of the subsystem. It hinders heat to be transferred from the slow-conducting polymer into the fast-conducting nanotubes and back into the polymer. This interpretation is in line with the majority of recent simulation works. An alternative explanation, namely, the damping of the long-wavelength phonons in nanotubes by the polymer matrix is not supported by the present calculations. These modes provide most of the polymers heat conduction. An additional minor effect is caused by the anisotropic structure of the polymer phase induced by the nearby nanotube surfaces. The thermal conductivity of the polymer matrix increases slightly in the direction parallel to the nanotubes, whereas it decreases perpendicular to it.