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

TL;DR

This study uses molecular dynamics simulations combined with machine learning models to accurately predict hydrogen diffusion in saline aquifers, considering various physical conditions and ion compositions.

Contribution

It introduces a hybrid approach integrating MD simulations with ML models, especially gradient boosting, for improved hydrogen diffusion prediction in brine environments.

Findings

Temperature, pressure, and ion properties influence hydrogen diffusion.

Arrhenius model is less accurate at high temperatures and pressures.

Gradient boosting model outperforms other ML models and Arrhenius predictions.

Abstract

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.