Perovskite-LLM：面向鈣鈦礦太陽能電池研究的知識增強型大型語言模型

Perovskite-LLM: Knowledge-Enhanced Large Language Models for Perovskite Solar Cell Research

February 18, 2025

作者: Xiang Liu, Penglei Sun, Shuyan Chen, Longhan Zhang, Peijie Dong, Huajie You, Yongqi Zhang, Chang Yan, Xiaowen Chu, Tong-yi Zhang

cs.AI

摘要

鈣鈦礦太陽能電池（PSCs）的快速發展導致了相關研究文獻的指數級增長，這使得該領域亟需高效的知識管理與推理系統。我們提出了一個全面的知識增強系統，專為PSCs設計，整合了三大核心組件。首先，我們構建了Perovskite-KG，這是一個基於1,517篇研究論文構建的領域特定知識圖譜，包含23,789個實體和22,272種關係。其次，我們創建了兩個互補的數據集：Perovskite-Chat，包含55,101對通過新穎的多智能體框架生成的高質量問答對；以及Perovskite-Reasoning，收錄了2,217個精心策劃的材料科學問題。第三，我們引入了兩個專用的大型語言模型：Perovskite-Chat-LLM，用於提供領域特定知識輔助；Perovskite-Reasoning-LLM，專注於科學推理任務。實驗結果表明，我們的系統在領域特定知識檢索和科學推理任務上均顯著優於現有模型，為研究人員在PSC研究中的文獻綜述、實驗設計及複雜問題解決提供了有效工具。

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.