Exploring the Capabilities of the Frontier Large Language Models for Nuclear Energy Research
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.
Keywords
Cite
@article{arxiv.2506.19863,
title = {Exploring the Capabilities of the Frontier Large Language Models for Nuclear Energy Research},
author = {Ahmed Almeldein and Mohammed Alnaggar and Rick Archibald and Tom Beck and Arpan Biswas and Rike Bostelmann and Wes Brewer and Chris Bryan and Christopher Calle and Cihangir Celik and Rajni Chahal and Jong Youl Choi and Arindam Chowdhury and Mark Cianciosa and Franklin Curtis and Gregory Davidson and Sebastian De Pascuale and Lisa Fassino and Ana Gainaru and Yashika Ghai and Luke Gibson and Qian Gong and Christopher Greulich and Scott Greenwood and Cory Hauck and Ehab Hassan and Rinkle Juneja and Soyoung Kang and Scott Klasky and Atul Kumar and Vineet Kumar and Paul Laiu and Calvin Lear and Yan-Ru Lin and Jono McConnell and Furkan Oz and Rishi Pillai and Anant Raj and Pradeep Ramuhalli and Marie Romedenne and Samantha Sabatino and José Salcedo-Pérez and Nathan D. See and Arpan Sircar and Punam Thankur and Tim Younkin and Xiao-Ying Yu and Prashant Jain and Tom Evans and Prasanna Balaprakash},
journal= {arXiv preprint arXiv:2506.19863},
year = {2025}
}