Using one of the world’s fastest supercomputers, researchers will simulate how fuel burns and hot gases flow through a jet engine’s combustor and turbine together—revealing physics that can boost efficiency, cut emissions, and enable tougher, longer-lasting designs.

This project uses powerful supercomputers to rapidly screen and simulate new battery liquids, helping scientists design safer, longer-lasting, high-energy batteries for cars and devices by understanding how lithium moves through next-generation electrolytes.

This project uses powerful supercomputers to simulate how bubbles—and soap-like additives that change their behavior—move and mix in fast, churning flows, yielding insights that can make reactors, heat exchangers, and cooling systems safer and more energy-efficient.

This project develops a resilient, human-like AI that learns and reasons on the fly—using supercomputers and diverse space and fusion data—to help satellites safely dodge debris and keep fusion reactors running smoothly, strengthening national security and accelerating clean energy.

TAE Technologies is using advanced supercomputer simulations to test and refine its field-reversed-configuration fusion approach—building on experiments that show wider stable operating ranges and better energy confinement at higher temperatures—to determine if these gains hold at power-plant conditions, reducing risk and bringing carbon-free fusion electricity closer to reality.

This project is creating an easy-to-use AI app that taps advanced simulations to quickly test and improve designs for safer, more efficient molten-salt nuclear reactors—reducing time, cost, and expertise needed and helping speed progress toward cleaner energy.

Using powerful supercomputers, researchers will run detailed simulations to map how and when turbulence starts and flows inside fusion plasmas—improving the models engineers use to design next-generation fusion pilot plants and speeding progress toward practical fusion energy.

The muon is a short-lived cousin of the electron that’s about 200 times heavier, and a landmark Fermilab experiment has measured its tiny “magnet strength” with extreme precision; to check whether our best physics theory still holds up, scientists now need equally precise calculations of how the muon’s interaction with the strong force affects that magnetism, using powerful supercomputer simulations.

This project aims to advance the accuracy and reliability of computer-based predictions for catalytic processes, which are vital for developing efficient and sustainable energy technologies. Leveraging state- of-the-art quantum Monte Carlo (QMC) methods and powerful supercomputing resources, the team will produce highly precise datasets to improve understanding and modeling of catalytic reactions, particularly where existing computational methods often fall short, or when the experiments are non-existent.

This project will develop the challenging capability for prediction and real-time control of energetic particle (EP) confinement in burning plasmas by combining the state-of-the-art exascale first-principles GTC simulation and the prominent experimentally validated AI/Deep Learning FRNN software