Capable exascale systems will be able to analyze massive volumes of data in less time, and power the advanced models and simulations required for discovering insights and answers to crucial scientific and technology challenges.
Scientific applications for high-performance and data analytic computing impact nearly every corner of research and development, from the physics of star explosions to squeezing the last percent of efficiency out of a jet engine.
Practical economic design of 1 TeV electron-positron high-energy collider with plasma wakefield acceleration.
Additive manufacturing process design for qualifiable metal components.
Demystify origin of chemical elements
(> Fe); confirm LIGO gravitational wave and DUNE neutrino signatures.
Scaling carbon capture/storage laboratory designs of multiphase reactors to industrial size.
Biofuel catalysts design; stress-resistant crops.
Accurate regional impact assessment of climate change.
Design high-efficiency, low-emission combustion engines and gas turbines.
Cosmological probe of standard model (SM) of particle physics: inflation, dark matter, and dark energy.
Safe and efficient use of subsurface for carbon capture and storage, petroleum extraction, geothermal energy, and nuclear waste.
Predict and guide stable ITER operational performance with an integrated, whole-device model.
Leveraging microbial diversity in metagenomic data sets for new products and life-forms.
Accelerate design and commercialization of next-generation small modular reactors.
Extend nuclear reactor fuel burnup and develop fusion reactor plasma-facing materials.
QCD-based elucidation of fundamental laws of nature: SM validation and beyond SM discoveries.
Reliably and efficiently planning our nation’s grid for societal drivers: rapidly increasing renewable energy penetration and more active consumers.
Accelerate and translate cancer research in RAS pathways, drug responses, and treatment strategies.