Los Alamos National Laboratory scientists are pioneering the use of powerful machine learning models to simulate underground hydrogen storage operations with cushion gas, a crucial element in building a low-carbon economy. Led by Mohamed Mehana, the team explores the impact of cushion gases such as methane, carbon dioxide, and nitrogen on hydrogen storage systems in deep saline aquifers and depleted hydrocarbon reservoirs. Their recent paper in the International Journal of Hydrogen Energy delves into comprehensive cushion gas scenarios, providing valuable insights for optimizing underground hydrogen storage performance. Through a deep neural network model, the team uncovers key findings on hydrogen storage in porous rocks, saline aquifers, and depleted reservoirs. The research emphasizes the necessity of maximizing hydrogen recoverability and purity while minimizing water production risks, with the goal of making underground hydrogen storage economically viable. This work builds on years of investigation at Los Alamos and showcases the potential of advanced AI models in revolutionizing hydrogen storage. With the nation's push towards decarbonization and the scaling of the hydrogen economy, this research plays a critical role in advancing clean energy technologies.