Los Alamos National Laboratory scientists are at the forefront of innovating underground hydrogen storage through the application of powerful machine learning models. Led by Mohamed Mehana, the team is focusing on simulating operations in deep saline aquifers or depleted hydrocarbon reservoirs with the injection of cushion gases to provide pressure support for hydrogen recovery. The recently published paper in the International Journal of Hydrogen Energy delves into the impact of various cushion gases on storage performance, shedding light on crucial insights for the hydrogen economy. The study emphasizes the importance of understanding the effects of methane, carbon dioxide, and nitrogen on underground hydrogen storage systems, aiming to maximize hydrogen recoverability and purity while mitigating water production risks. By employing a deep neural network machine learning model, the team explores the technical promise of hydrogen storage in porous rocks and the implications of storage in saline aquifers and depleted hydrocarbon reservoirs. This research represents a significant advancement in the field, with Los Alamos being a pioneer in exploring the complexities of underground hydrogen storage. Their work combines geological studies with machine learning simulations to analyze the flow and transport behavior of hydrogen, study potential storage locations, and develop tools for assessing hydrogen storage reliability and performance. The development of OPERATE-H2, the first industry-available software integrating machine learning for optimizing hydrogen storage, showcases the lab's commitment to sustainable energy solutions.