Genesis Mission

Featured Initiatives

Environment Management

SRNL researchers are leading a collaborative effort to advance the  Department of Energy’s mission to transform Nuclear Cleanup for Restoration and Revitalization (NR²) through the application of AI. The initiative is supported by the DOE’s Network of National Laboratories for Environmental Management and Stewardship (NNLEMS).

The DOE Office of Environmental Management faces an estimated $540 billion dollar liability spanning eight decades and involving processing of 90 million gallons of highly radioactive waste, 6.5 trillion liters of contaminated groundwater, and 40 million cubic meters of contaminated soil. To reduce cost, risk, and long-term liabilities associated with these challenges, SRNL is leading the development of DOE-EM’s AI Research and Development Roadmap, which seeks to guide AI investments across all mission areas at the 15 DOE-EM sites.

Additionally, SRNL has been active in leading research and development efforts for environmental cleanup missions, through the use of integrated sensing technologies.  As part of the Advanced Long-Term Environmental Monitoring Systems (ALTEMIS) program, the team developed end-to-end AI workflows for the design and optimization of sensor networks that predict the behavior and movement of contaminated groundwater plumes. This allows clean-up efforts to go from reactive to proactive resulting in significant cost savings. Coined AI Accelerated Strategies and Solutions in Environmental Technology, or AI-ASSET, the team seeks to transition the ALTEMIS approach to new sites and new systems in the coming year.

Critical Materials & Materials to Unlock Supply (CM²US)

SRNL is participating in the CM²US (Critical Minerals & Materials to Unlock Supply) initiative, a multi-national laboratory collaboration that uses artificial intelligence and advanced data platforms to accelerate discovery, processing, and deployment of critical minerals across the supply chain – from mining to manufacturing. 

Working with tech innovators at Silica X, Inc., SRNL is solving a key challenge: turning leftover materials of processes across the supply chain into valuable resources. Using an advanced AI tool called Wastepoint.AI, we will study the entire lifecycle of a critical material—from creation to reuse—and pinpoint practical ways to transform waste and byproducts into useful, saleable products to support supply chains and innovation.

In 2026, the SRNL team will:

• Release an initial report displaying an AI that can suggest cost-saving methodologies and improve efficiency in the development and optimization of one or more critical material lifecycles.
• Analyze cutting-edge technologies to demonstrate how the AI platform bridges scientific expertise with real-world applications.
• Present a detailed report explaining how Wastepoint.AI outperforms traditional methods and what data is essential to adapt it for different material streams.

This effort is a forward-thinking approach to reducing waste and creating opportunities in material production and reuse.

Transformational AI Model Consortium

SRNL is advancing the Genesis Mission’s Transformational AI Model Consortium (ModCon) through a focused effort to develop AI-ready data capabilities that enable next-generation scientific and operational models.  Within the Genesis Mission, SRNL’s contribution centers on preparing, integrating, and governing high-value mission datasets so they can support the development and deployment of self-improving AI models.

By leveraging DOE’s unique assets, research facilities and domain expertise, SRNL and its partners are working to ensure critical data are structured, curated, and accessible for advanced AI applications. These efforts will establish foundational data capabilities needed across multiple scientific and engineering domains, including core SRNL capabilities, such as environmental remediation, nuclear materials processing, energy systems, and national security missions. These efforts also will enable collaborative development of transformational AI models across the DOE enterprise. 

Artificial Intelligence for Nuclear Security

Additionally, SRNL is contributing to the Genesis Mission by applying Artificial Intelligence and Machine Learning technologies to optimize, enhance and potentially transform the future of National Nuclear Security Administration’s national security missions. For more than a decade, NNSA’s early investments in AI have helped to demonstrate prototype capabilities that will deepen scientific insights, accelerate design, manufacturing and engineering, and aid in the partial automation of time-consuming tasks across the nuclear security enterprise.

In FY26, the Artificial Intelligence for Nuclear Security (AI4NS) sub-program of the NNSA’s Office of Advanced Simulation and Computing awarded ~$50M (21 projects) across 13 DOE labs, plants, and sites seeking to apply AI and/or ML capabilities to a narrowly focused set of national security mission targets. SRNL researchers are leading two of those projects: one targeting AI for Manufacturing and Qualification and the other targeting AI for Materials Discovery in collaboration with the Nevada National Security Sites (NNSS).

Advancing Fusion Energy Systems

SRNL is collaborating with other national laboratories as part of the American Science Cloud initiative to advance the development of fusion energy. The project aims to create a versatile digital platform that integrates real-time and simulated data to accelerate the design and deployment of Inertial Fusion Energy (IFE) systems, which will optimize the development and commercialization of future fusion power plants.

One of the key elements of Inertial Fusion Energy systems is the hydrogen isotope tritium. SRNL is leveraging its unparalleled expertise in tritium science to generate high-quality datasets and build AI-driven models to simulate tritium flow, storage, and recycling. In partnership with Lawrence Livermore National Laboratory, SRNL is using these datasets to develop and validate machine learning models that ensure tritium handling is efficient and meets regulatory standards. By contributing advanced tritium models and technical expertise, SRNL is playing a vital role in enabling rapid innovation and deployment of fusion energy technologies.