为钙钛矿太阳能电池研究构建知识增强型大模型系统
Perovskite-LLM: Knowledge-Enhanced Large Language Models for Perovskite Solar Cell Research
- 基于1517篇论文构建专用知识图谱，含2.3万实体与2.2万关系
- 开发问答与科学推理双数据集，支持高效文献检索与复杂问题求解
- 推出两款垂直领域大模型，显著提升科研场景下的知识获取与推理能力
钙钛矿太阳能电池（PSCs）的快速发展带来了研究论文的指数级增长，亟需高效的领域知识管理与推理系统。本文提出一个全面的知识增强系统，包含三大核心组件：首先，构建了从1,517篇论文中提取的领域专属知识图谱Perovskite-KG，包含23,789个实体和22,272条关系；其次，设计两个互补数据集：Perovskite-Chat（55,101个高质量问答对）与Perovskite-Reasoning（2,217个精心筛选的材料科学问题）；第三，提出两款专用大语言模型：Perovskite-Chat-LLM用于领域知识辅助，Perovskite-Reasoning-LLM用于科学推理任务。实验表明，该系统在领域知识检索与科学推理任务上均显著优于现有模型，为研究人员提供文献综述、实验设计与复杂问题求解的有效工具。
原文摘要 · Abstract (English)
The rapid advancement of perovskite solar cells (PSCs) has led to an exponential growth in research publications, creating an urgent need for efficient knowledge management and reasoning systems in this domain. We present a comprehensive knowledge-enhanced system for PSCs that integrates three key components. First, we develop Perovskite-KG, a domain-specific knowledge graph constructed from 1,517 research papers, containing 23,789 entities and 22,272 relationships. Second, we create two complementary datasets: Perovskite-Chat, comprising 55,101 high-quality question-answer pairs generated through a novel multi-agent framework, and Perovskite-Reasoning, containing 2,217 carefully curated materials science problems. Third, we introduce two specialized large language models: Perovskite-Chat-LLM for domain-specific knowledge assistance and Perovskite-Reasoning-LLM for scientific reasoning tasks. Experimental results demonstrate that our system significantly outperforms existing models in both domain-specific knowledge retrieval and scientific reasoning tasks, providing researchers with effective tools for literature review, experimental design, and complex problem-solving in PSC research.
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