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

TL;DR

This paper introduces SELLM, a framework that uses large language models and structured guidance to generate effective, cross-disciplinary solutions for complex scientific challenges, demonstrated through case studies in electronics.

Contribution

The paper presents a novel framework, SELLM, which systematically leverages LLMs and MECE principles to produce effective solutions for interdisciplinary problems.

Findings

SELLM enhances solution quality over unstructured approaches

Effective solutions were generated for OLED lighting and memory electrodes

Structured guidance improves LLMs' problem-solving capabilities

Abstract

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.