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

Deep saline aquifers are some 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 the modeling of hydrogen storage. The diffusion coefficient of hydrogen molecules in chloride brine with different cations (Na+, K+, and Ca2+) containing up to 5 mol/kgH2O concentration is numerically investigated using molecular dynamics (MD) simulation. A wide range of pressure (1–218 atm) and temperature (298–648 K) conditions are 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 by 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 (≥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 the 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. Our work provides a promising route for a quick and cost-effective diffusion coefficient determination for multiple and complex brine solutions with a wide range of temperature, pressure, and ion concentration by the combination of MD simulations and ML techniques.