A comprehensive review of green hydrogen production via electrolysis and thermolysis, and the prediction of potential natural hydrogen (aka gold hydrogen) presence using machine learning

The global push toward net-zero carbon emissions has heightened interest in hydrogen as a clean energy carrier. This review explores the challenge of establishing sustainable hydrogen production pathways by focusing on two pivotal forms: green hydrogen, produced from renewable sources through water splitting, and gold hydrogen, a naturally occurring resource found in subsurface environments. For green hydrogen, production methods based on electrolysis and thermochemical water-splitting cycles are reviewed, with emphasis on catalyst-driven technological advances and solar-powered electrolysis and thermolysis. To evaluate the potential for solar-powered water splitting, a comparative analysis of 30 countries, selected for data reliability and diversity in solar resource availability, indicates that 60 % are well-positioned for green hydrogen deployment, whereas 40 % encounter substantial resource-related limitations. In these constrained regions, gold hydrogen emerges as a promising alternative. This review identifies the key geological factors influencing the potential for its presence and examines the application of machine learning (ML) techniques to predict its spatial distribution. The analysis reveals that specific machine learning models can successfully identify patterns within geological data, aiding in the preliminary strategic selection of sites for future gold hydrogen exploration. Among the 11 regression algorithms evaluated, the Decision Tree Regressor exhibited the best performance, indicating the presence of pronounced hierarchical structures. Similarly, the Gradient Boosting Regressor provided additional reliability through its ensemble-based approach. These algorithms provide preliminary predictions of potential gold hydrogen occurrences in resource-scarce regions, making this ML-based approach both novel and highly applicable to industrial and research contexts.