Interfacial Tension–Temperature–Pressure–Salinity Relationship for the Hydrogen–Brine System under Reservoir Conditions: Integration of Molecular Dynamics and Machine Learning

[Image: see text] Hydrogen (H(2)) underground storage has attracted considerable attention as a potentially efficient strategy for the large-scale storage of H(2). Nevertheless, successful execution and long-term storage and withdrawal of H(2) necessitate a thorough understanding of the physical and chemical properties of H(2) in contact with the resident fluids. As capillary forces control H(2) migration and trapping in a subsurface environment, quantifying the interfacial tension (IFT) between H(2) and the resident fluids in the subsurface is important. In this study, molecular dynamics (MD) simulation was employed to develop a data set for the IFT of H(2)–brine systems under a wide range of thermodynamic conditions (298–373 K temperatures and 1–30 MPa pressures) and NaCl salinities (0–5.02 mol·kg(–1)). For the first time to our knowledge, a comprehensive assessment was carried out to introduce the most accurate force field combination for H(2)–brine systems in predicting interfacial properties with an absolute relative deviation (ARD) of less than 3% compared with the experimental data. In addition, the effect of the cation type was investigated for brines containing NaCl, KCl, CaCl(2), and MgCl(2). Our results show that H(2)–brine IFT decreases with increasing temperature under any pressure condition, while higher NaCl salinity increases the IFT. A slight decrease in IFT occurs when the pressure increases. Under the impact of cation type, Ca(2+) can increase IFT values more than others, i.e., up to 12% with respect to KCl. In the last step, the predicted IFT data set was used to provide a reliable correlation using machine learning (ML). Three white-box ML approaches of the group method of data handling (GMDH), gene expression programming (GEP), and genetic programming (GP) were applied. GP demonstrates the most accurate correlation with a coefficient of determination (R(2)) and absolute average relative deviation (AARD) of 0.9783 and 0.9767%, respectively.