Exploring the Capabilities of the Frontier Large Language Models for Nuclear Energy Research

• URL: http://arxiv.org/abs/2506.19863v2
• Date: Thu, 26 Jun 2025 22:36:10 GMT
• Title: Exploring the Capabilities of the Frontier Large Language Models for Nuclear Energy Research
• Authors: Ahmed Almeldein, Mohammed Alnaggar, Rick Archibald, Tom Beck, Arpan Biswas, Rike Bostelmann, Wes Brewer, Chris Bryan, Christopher Calle, Cihangir Celik, Rajni Chahal, Jong Youl Choi, Arindam Chowdhury, Mark Cianciosa, Franklin Curtis, Gregory Davidson, Sebastian De Pascuale, Lisa Fassino, Ana Gainaru, Yashika Ghai, Luke Gibson, Qian Gong, Christopher Greulich, Scott Greenwood, Cory Hauck, Ehab Hassan, Rinkle Juneja, Soyoung Kang, Scott Klasky, Atul Kumar, Vineet Kumar, Paul Laiu, Calvin Lear, Yan-Ru Lin, Jono McConnell, Furkan Oz, Rishi Pillai, Anant Raj, Pradeep Ramuhalli, Marie Romedenne, Samantha Sabatino, José Salcedo-Pérez, Nathan D. See, Arpan Sircar, Punam Thankur, Tim Younkin, Xiao-Ying Yu, Prashant Jain, Tom Evans, Prasanna Balaprakash,
• Abstract summary: The AI for Nuclear Energy workshop at Oak Ridge National Laboratory evaluated the potential of Large Language Models (LLMs) to accelerate fusion and fission research.<n>14 interdisciplinary teams explored diverse nuclear science challenges using ChatGPT, Gemini, Claude, and other AI models over a single day.<n>The successful outcomes resulted from expert-driven prompt engineering and treating AI as a complementary tool rather than a replacement for physics-based methods.
• Score: 9.756086474348352
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The AI for Nuclear Energy workshop at Oak Ridge National Laboratory evaluated the potential of Large Language Models (LLMs) to accelerate fusion and fission research. Fourteen interdisciplinary teams explored diverse nuclear science challenges using ChatGPT, Gemini, Claude, and other AI models over a single day. Applications ranged from developing foundation models for fusion reactor control to automating Monte Carlo simulations, predicting material degradation, and designing experimental programs for advanced reactors. Teams employed structured workflows combining prompt engineering, deep research capabilities, and iterative refinement to generate hypotheses, prototype code, and research strategies. Key findings demonstrate that LLMs excel at early-stage exploration, literature synthesis, and workflow design, successfully identifying research gaps and generating plausible experimental frameworks. However, significant limitations emerged, including difficulties with novel materials designs, advanced code generation for modeling and simulation, and domain-specific details requiring expert validation. The successful outcomes resulted from expert-driven prompt engineering and treating AI as a complementary tool rather than a replacement for physics-based methods. The workshop validated AI's potential to accelerate nuclear energy research through rapid iteration and cross-disciplinary synthesis while highlighting the need for curated nuclear-specific datasets, workflow automation, and specialized model development. These results provide a roadmap for integrating AI tools into nuclear science workflows, potentially reducing development cycles for safer, more efficient nuclear energy systems while maintaining rigorous scientific standards.

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