标题： 通过生成综合专家提取大型语言模型中的有效解决方案：在开发电子设备中的案例研究 显示英文标题

标题： Extracting effective solutions hidden in large language models via generated comprehensive specialists: case studies in developing electronic devices

摘要： 最近，许多研究越来越多地探索使用大型语言模型（LLMs）来生成研究想法和科学假设。 然而，现实世界的研究和开发通常需要解决复杂的跨学科挑战，这些问题的解决方案可能无法通过与问题相关的现有知识轻易找到。 因此，利用LLMs庞大的综合知识，通过整合其他学科的各种观点，生成有效且突破性的解决方案是很有必要的。 在此，我们提出了SELLM（通过全面列表和LLM进行解决方案枚举）框架，该框架利用LLMs和使用MECE（相互独立、完全穷尽）原则的结构化指导，例如国际专利分类（IPC）和元素周期表。 SELLM系统地从列表中构建全面的专家代理，以生成跨学科的有效解决方案。 为了评估SELLM的实用性，我们将它应用于两个挑战：提高有机发光二极管（OLED）照明中的光提取效率以及开发下一代存储材料的电极。 结果表明，与没有特定定制或努力的情况相比，SELLM显著促进了有效解决方案的生成，展示了SELLM使LLMs即使在具有挑战性的问题上也能生成有效解决方案的潜力。 显示英文摘要

摘要： Recently, many studies have increasingly explored the use of large language models (LLMs) to generate research ideas and scientific hypotheses. However, real-world research and development often require solving complex, interdisciplinary challenges where solutions may not be readily found through existing knowledge related to the problem. Therefore, it is desirable to leverage the vast, comprehensive knowledge of LLMs to generate effective, breakthrough solutions by integrating various perspectives from other disciplines. Here, we propose SELLM (Solution Enumeration via comprehensive List and LLM), a framework leveraging LLMs and structured guidance using MECE (Mutually Exclusive, Collectively Exhaustive) principles, such as International Patent Classification (IPC) and the periodic table of elements. SELLM systematically constructs comprehensive expert agents from the list to generate cross-disciplinary and effective solutions. To evaluate SELLM's practicality, we applied it to two challenges: improving light extraction in organic light-emitting diode (OLED) lighting and developing electrodes for next-generation memory materials. The results demonstrate that SELLM significantly facilitates the generation of effective solutions compared to cases without specific customization or effort, showcasing the potential of SELLM to enable LLMs to generate effective solutions even for challenging problems.