The U.S. Department of Energy is deploying artificial intelligence to solve one of the nation's most complex environmental challenges: safely managing decades of nuclear waste and contaminated sites. Savannah River National Laboratory (SRNL) has received funding through the DOE's Genesis Mission to build AI-powered systems that predict how radioactive materials behave during treatment and storage, potentially unlocking over $150 billion in lifecycle cleanup savings.

What Is the Genesis Mission and Why Does It Matter?

The Genesis Mission represents a historic collaboration between government, industry, academia, and philanthropy to create what the DOE calls "the world's most powerful integrated science discovery platform." Rather than relying on traditional laboratory experiments alone, the initiative combines artificial intelligence, supercomputing, quantum systems, and advanced scientific instruments to accelerate breakthroughs in energy, scientific discovery, and national security.

For SRNL, this partnership leverages more than 70 years of expertise in nuclear sciences combined with unique, site-specific datasets to develop first-of-their-kind AI models. These tools are designed to solve complex challenges that have historically required expensive, time-consuming physical testing and analysis.

How Are AI Models Being Used to Manage Nuclear Waste?

Two flagship projects demonstrate the practical applications of AI in environmental remediation. The first, called VITA-SCALE and led by SRNL researcher Nathan Morgan, uses physics-based artificial intelligence to predict how nuclear waste behaves during vitrification, the process of converting waste materials into glass for long-term storage. Traditional approaches rely on small-scale laboratory analyses and steady-state models that don't capture real-world complexity at full scale. VITA-SCALE overcomes these limitations, accelerating cleanup timelines, reducing costs, and improving safety. The project plans to open-source its models to encourage broader innovation across the cleanup industry.

The second project, called SCOPE and led by SRNL's Tom Danielson, combines artificial intelligence with chemistry and physics-based modeling to predict the behavior of radioactive liquid waste during treatment. This improved understanding helps prevent costly operational disruptions, such as unexpected solids formation, while improving process efficiency and reducing operational risk.

"SRNL has built decades of expertise tackling complex environmental cleanup and nuclear materials challenges. As we embark on the Genesis Mission, we are eager to harness this depth of expertise in collaboration with our partners across national laboratories, industry, and academic institutions. By integrating our longstanding scientific knowledge with emerging AI capabilities, we are accelerating discovery in ways that will strengthen our national security, environmental, and energy missions," said Johney Green, SRNL Director.

Johney Green, Director at Savannah River National Laboratory

Steps to Integrate AI Into Environmental Remediation Projects

The Genesis Mission's Phase I awards focus on establishing research workflows that demonstrate how AI can enhance scientific discovery. Project teams are following a structured approach to validate whether AI-driven methods can deliver measurable benefits:

• Design Integration Workflows: Teams develop processes that combine artificial intelligence with traditional scientific investigation, ensuring AI tools complement rather than replace domain expertise.
• Evaluate Predictive Capabilities: Researchers rigorously test whether AI models improve accuracy in forecasting material behavior, waste treatment outcomes, and infrastructure resilience compared to conventional methods.
• Enhance Experimentation: AI systems are deployed to optimize experimental design, reduce the number of physical tests required, and accelerate the pace of discovery.
• Generate New Scientific Insights: By analyzing large datasets and identifying patterns humans might miss, AI models reveal previously unknown relationships between variables in complex environmental systems.

SRNL is not working in isolation. The laboratory is partnering on additional projects led by Pacific Northwest National Laboratory and Vanderbilt University, demonstrating how the Genesis Mission brings together expertise from multiple institutions to tackle national challenges.

Why Does This Matter Beyond Nuclear Cleanup?

The potential $150 billion in lifecycle cleanup savings represents far more than a budget reduction. Environmental remediation at DOE sites has been a decades-long challenge, with contaminated groundwater, radioactive waste, and aging infrastructure requiring constant attention. By accelerating cleanup timelines and improving safety, these AI models could free up resources for other national priorities while reducing the environmental footprint of legacy nuclear programs.

The Genesis Mission's approach also establishes a template for how government laboratories can leverage AI to solve other complex scientific problems. Rather than treating AI as a standalone tool, the initiative emphasizes integration with supercomputing, quantum systems, and traditional scientific expertise. This collaborative model could reshape how federal research institutions approach materials science, energy innovation, and infrastructure challenges in the years ahead.

SRNL Director Johney Green and computational scientist Tom Danielson attended the Genesis Mission Summit on July 22, 2026, where these projects were highlighted as part of the DOE's broader commitment to accelerating scientific discovery through advanced computing and artificial intelligence.