The Department of Energy’s Oak Ridge National Laboratory recently hosted the AI for Nuclear Energy Workshop, which explored the ways in which AI can be used to address nuclear energy challenges and accelerate fusion and fission research.
Experts in nuclear energy research, computational science, materials science and AI gathered at ORNL on May 1, 2025, forming 14 interdisciplinary teams. Each team used models like ChatGPT, Gemini, Claude and others to evaluate their effectiveness in applications such as developing foundation models for fusion reactor control, automating Monte Carlo simulations — used to better predict the behavior of complex systems — and predicting material degradation.
The primary goal of the workshop was to test the capabilities of large language models and identify their limitations regarding their use in nuclear energy research.
Throughout the day, the 14 teams explored how AI tools can be used to complement traditional physics-based approaches to address current nuclear energy challenges. Prasanna Balaprakash, head of the Data and AI Systems Research Section in the Computer Science and Mathematics Division at ORNL, stressed the need among those who participated for this type of research to provide a greater understanding of the strengths and limitations of Frontier AI models so that nuclear energy research can be accelerated in light of the global race for energy dominance.
“Bringing together nuclear energy researchers, engineers, computational scientists and AI experts for a day of hands-on collaboration revealed both the promise and the limitations of current large language and reasoning models,” Balaprakash said. “The workshop highlighted the real value of accelerating early-stage research through AI-driven hypothesis generation and workflow design but also reaffirmed the essential role of domain expertise and curated data. An essential takeaway from the workshop is the importance of developing synergy between domain experts and AI tools to accelerate nuclear energy innovation.”
At the conclusion of the workshop, the overall consensus among participants was that advanced AI models with reasoning capabilities have the potential to become an invaluable tool and an innovation driver in fusion and fission energy research, from plasma control to neutronics modeling and data management.
There are, however, some key limitations to using AI in these research areas. These limitations include data and code access barriers, data access and validation, detailed implementation and technical accuracy. Despite these limitations, the workshop demonstrated that AI can be an accelerator for early-stage nuclear research, especially in rapid literature synthesis, knowledge integration and initial hypothesis generation.
“Cutting edge research requires cutting-edge tools,” said Mickey Wade, associate lab director for ORNL’s Fusion and Fission Energy and Science Directorate. “ORNL’s diverse R&D portfolio across high-performance computing, artificial intelligence, quantum computing and fusion and fission energy research enables the cross-disciplinary approach that contributes to breakthrough science.”
The workshop identified several key future research directions for using AI for innovation in nuclear energy research. These include the development of domain-specialized foundation models, physics-informed surrogates for high-fidelity simulations, and autonomous, agentic workflows that integrate theory, simulation and experimentation. Emphasis was also placed on assured AI through uncertainty quantification, causal reasoning and regulatory-aligned validation to support safe and efficient deployment in nuclear applications.
The workshop report is available here.
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UT-Battelle manages ORNL for the Department of Energy’s Office of Science, the single largest supporter of basic research in the physical sciences in the United States. The Office of Science is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science. — Mark Alewine