September 17, 2025

A Comprehensive AI-Model for H2-Brine Interfacial Tension Prediction: Implications for Underground Hydrogen Storage

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The machine learning models achieved over 96% accuracy in predicting interfacial tension, enhancing hydrogen storage safety.
The random forest model resulted in a mean absolute percentage error of 1.88% during training, reflecting its robustness.
The study utilized a comprehensive dataset of H2-brine interactions across various conditions, expanding predictive capabilities.
Findings highlight the importance of generalized input parameters for accurately modeling fluid interactions in hydrogen storage.

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Machine learning predicts interfacial tension in underground hydrogen storage, suggesting improved efficiency.

Ahmed Farid Ibrahim (2025) studied this question.

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