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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2506.19863 |
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| _version_ | 1866915361003143168 |
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| author | Almeldein, Ahmed Alnaggar, Mohammed Archibald, Rick Beck, Tom Biswas, Arpan Bostelmann, Rike Brewer, Wes Bryan, Chris Calle, Christopher Celik, Cihangir Chahal, Rajni Choi, Jong Youl Chowdhury, Arindam Cianciosa, Mark Curtis, Franklin Davidson, Gregory De Pascuale, Sebastian Fassino, Lisa Gainaru, Ana Ghai, Yashika Gibson, Luke Gong, Qian Greulich, Christopher Greenwood, Scott Hauck, Cory Hassan, Ehab Juneja, Rinkle Kang, Soyoung Klasky, Scott Kumar, Atul Kumar, Vineet Laiu, Paul Lear, Calvin Lin, Yan-Ru McConnell, Jono Oz, Furkan Pillai, Rishi Raj, Anant Ramuhalli, Pradeep Romedenne, Marie Sabatino, Samantha Salcedo-Pérez, José See, Nathan D. Sircar, Arpan Thankur, Punam Younkin, Tim Yu, Xiao-Ying Jain, Prashant Evans, Tom Balaprakash, Prasanna |
| author_facet | Almeldein, Ahmed Alnaggar, Mohammed Archibald, Rick Beck, Tom Biswas, Arpan Bostelmann, Rike Brewer, Wes Bryan, Chris Calle, Christopher Celik, Cihangir Chahal, Rajni Choi, Jong Youl Chowdhury, Arindam Cianciosa, Mark Curtis, Franklin Davidson, Gregory De Pascuale, Sebastian Fassino, Lisa Gainaru, Ana Ghai, Yashika Gibson, Luke Gong, Qian Greulich, Christopher Greenwood, Scott Hauck, Cory Hassan, Ehab Juneja, Rinkle Kang, Soyoung Klasky, Scott Kumar, Atul Kumar, Vineet Laiu, Paul Lear, Calvin Lin, Yan-Ru McConnell, Jono Oz, Furkan Pillai, Rishi Raj, Anant Ramuhalli, Pradeep Romedenne, Marie Sabatino, Samantha Salcedo-Pérez, José See, Nathan D. Sircar, Arpan Thankur, Punam Younkin, Tim Yu, Xiao-Ying Jain, Prashant Evans, Tom Balaprakash, Prasanna |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_19863 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploring the Capabilities of the Frontier Large Language Models for Nuclear Energy Research Almeldein, Ahmed Alnaggar, Mohammed Archibald, Rick Beck, Tom Biswas, Arpan Bostelmann, Rike Brewer, Wes Bryan, Chris Calle, Christopher Celik, Cihangir Chahal, Rajni Choi, Jong Youl Chowdhury, Arindam Cianciosa, Mark Curtis, Franklin Davidson, Gregory De Pascuale, Sebastian Fassino, Lisa Gainaru, Ana Ghai, Yashika Gibson, Luke Gong, Qian Greulich, Christopher Greenwood, Scott Hauck, Cory Hassan, Ehab Juneja, Rinkle Kang, Soyoung Klasky, Scott Kumar, Atul Kumar, Vineet Laiu, Paul Lear, Calvin Lin, Yan-Ru McConnell, Jono Oz, Furkan Pillai, Rishi Raj, Anant Ramuhalli, Pradeep Romedenne, Marie Sabatino, Samantha Salcedo-Pérez, José See, Nathan D. Sircar, Arpan Thankur, Punam Younkin, Tim Yu, Xiao-Ying Jain, Prashant Evans, Tom Balaprakash, Prasanna Computational Physics Artificial Intelligence 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. |
| title | Exploring the Capabilities of the Frontier Large Language Models for Nuclear Energy Research |
| topic | Computational Physics Artificial Intelligence |
| url | https://arxiv.org/abs/2506.19863 |