Today, the U.S. Department of Energy announced funding for new projects that will harness artificial intelligence to accelerate discovery. Lawrence Berkeley National Laboratory (Berkeley Lab) will lead 13 projects and collaborate as a partner on more than 30 others.
The projects are part of DOE's Genesis Mission, a historic national initiative that aims to build the world's most powerful integrated science discovery platform. By uniting government, industry, academia, and philanthropy, it seeks to accelerate breakthroughs in energy, scientific discovery, and national security through a new platform that combines AI, supercomputing, quantum systems, and advanced scientific instruments.
Berkeley Lab's contributions to the Genesis Mission build on decades of research in high-performance computing, managing large datasets, and pioneering AI models. Researchers will lead new projects across multiple disciplines, advancing fields including critical minerals, materials, manufacturing, fusion, and energy.
"By developing advanced AI tools and combining them with high-quality datasets and high-performance computing, our researchers are maximizing their scientific impact and speeding up discovery," said Kathy Yelick, director of Berkeley Lab. "Beyond that, the Genesis Mission is also an opportunity to pioneer better AI tools and approaches for all of science, which will bring payoffs for generations to come."
The goal of the Phase 1 Genesis Mission projects is to identify promising pathways toward transformative scientific capabilities and establish a foundation for future investment and scale. Project teams will design and demonstrate research workflows that integrate AI with scientific investigation, while rigorously evaluating whether those approaches can accelerate discovery, improve predictive capabilities, enhance experimentation, or generate new scientific insights.
The projects that Berkeley Lab leads are:
- AI Decodes DOE Data to Protect U.S. Hydropower and Grid: ASPIRE will create an AI-driven workflow using Department of Energy data to improve precipitation forecasting, helping secure U.S. hydropower generation and protecting critical grid infrastructure.
- AI-Driven Autonomous Labs for Accelerated Materials Discovery: Researchers will create AI systems that work with autonomous laboratories to design, perform, and learn from experiments, dramatically speeding materials discovery and scientific innovation.
- AI-Powered Discovery of Quantum and Optoelectronic Materials: AI powered by physics simulations will model excited states to identify promising quantum and optoelectronic materials, accelerating research for future computing, sensing, and energy technologies.
- AI to Predict Water Supplies for Energy with Novel Precision: Scientists will develop an artificial intelligence framework to jointly predict surface and groundwater dynamics nationwide, supporting energy and water security.
- AI Workflows to Run DOE Codes in the Cloud: The Agentic HPC Pipeline Initiative (AHPI) will build AI workflows that enable U.S. manufacturers to run powerful DOE research codes on commercial cloud platforms.
- Breaking Silos: AI for Nuclear Waste Repository Design: AI will enable a streamlined environment to analyze nuclear waste repository design, dissolving existing silos through a digital-twin approach.
- Closing the Loop: AI-Accelerated Alloy Engineering: Researchers will combine AI structure prediction, high-throughput synthesis, and advanced microscopy to accelerate the optimization and design of complex concentrated alloys for extreme environmental applications.
- HERALD: AI to Unlock Nuclear Archives: The HERALD project will build an AI platform to vet decades of secure government research, unlocking it for the commercial industry to accelerate nuclear innovation.
- Meeting Water-Energy Demand with Local, Actionable Predictions: To predict water resources for energy infrastructure, scientists will use AI to develop a highly accurate prediction system for water availability.
- Novel AI Workflow for Critical Minerals in Geosystems: The team will use AI to optimize recovery of critical minerals from copper, molybdenum, platinum, chrome, and nickel mineral deposits.
- Physics-informed Digital Twins for Fusion Magnet Systems: AI-enabled digital twins will establish a credible path toward the reliable operation of high-temperature superconducting magnets, supporting future compact magnetic confinement fusion power plants.
- Predicting Subsurface Fractures with AI for Energy: Scientists will combine artificial intelligence and geophysical observations to improve prediction of subsurface fractures, advancing geothermal and other energy technologies.
- Safely Storing Nuclear Waste with Advanced 3D Models: Researchers will develop AI-assisted 3D geological models that can effectively identify suitable sites for subsurface nuclear waste storage.
