Los Alamos National Laboratory scientists, led by Mohamed Mehana, are leveraging powerful machine learning models to simulate underground hydrogen storage operations. Their focus is on the impact of cushion gases like methane, carbon dioxide, and nitrogen on the performance of hydrogen storage systems in deep saline aquifers and depleted hydrocarbon reservoirs. By studying various cushion gas scenarios, the team aims to optimize recoverability, purity, and operational risks associated with underground hydrogen storage. The research, detailed in a paper published by the International Journal of Hydrogen Energy, highlights the significance of understanding the effects of cushion gases to enhance the viability of underground hydrogen storage. By utilizing a deep neural network machine learning model, the team analyzed different geological and operational parameters to mimic real-world conditions, uncovering key insights. Los Alamos' extensive investigation represents a critical step in scaling the hydrogen economy for a low-carbon future. Their multi-disciplinary approach accounts for diverse geological conditions, water presence, and cushion gas impacts, emphasizing the complexity of underground hydrogen storage. The team's effort to maximize recoverability and purity while minimizing water production risks showcases the depth of their research. This breakthrough builds on Los Alamos' years of pioneering work in hydrogen storage research. Through exploring subsurface environments and combining machine learning simulations, the team has advanced the understanding of cushion gas effects on hydrogen storage. Additionally, they have identified potential storage locations and developed industry-available software for optimizing hydrogen storage. In conclusion, Los Alamos' innovative use of machine learning in hydrogen storage research signals a significant advancement in the quest for a sustainable, low-carbon economy. Their dedication to comprehensive studies and technological development positions them at the forefront of hydrogen storage research.