Perovskite-LLM: Knowledge-Enhanced Large Language Models for Perovskite
  Solar Cell Research
Authors: name: Xiang Liu, name: Penglei Sun, name: Shuyan Chen, name: Longhan Zhang, name: Peijie Dong, name: Huajie You, name: Yongqi Zhang, name: Chang Yan, name: Xiaowen Chu, name: Tong-yi Zhang

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.