探索前沿大型语言模型在核能研究中的能力
Exploring the Capabilities of the Frontier Large Language Models for Nuclear Energy Research
AI总结：
本研究评估了ChatGPT等大型语言模型在加速核能研究中的潜力，发现其擅长早期探索与工作流设计，但在高级代码与新材料设计上存在局限，需结合专家验证作为补充工具以缩短开发周期。
摘要：
橡树岭国家实验室的AI for Nuclear Energy研讨会评估了大型语言模型（LLMs）加速聚变与裂变研究的潜力。14个跨学科团队在一天内使用ChatGPT、Gemini、Claude及其他AI模型探索了各种核科学挑战。应用范围涵盖开发用于聚变反应堆控制的基础模型、自动化蒙特卡洛模拟、预测材料退化，以及为先进反应堆设计实验方案。各团队采用结合了提示工程、深度研究能力和迭代优化的结构化工作流，以生成假设、原型代码和研究策略。主要发现表明，LLMs擅长早期探索、文献综合和工作流设计，能成功识别研究空白并生成可行的实验框架。然而，也出现了显著的局限性，包括在新型材料设计、用于建模与模拟的高级代码生成，以及需要专家验证的特定领域细节方面存在困难。成功的结果源于专家驱动的提示工程，以及将AI视为基于物理学方法的补充工具而非替代品。该研讨会验证了AI通过快速迭代和跨学科综合来加速核能研究的潜力，同时强调需要精心整理的核专用数据集、工作流自动化和专门的模型开发。这些结果为将AI工具整合到核科学工作流中提供了路线图，有望缩短更安全、更高效的核能系统的开发周期，同时保持严格的科学标准。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
