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

Xiang Liu, sunpenglei, Shuyan Chen, Longhan Zhang, PeijieDong, Huajie You, yongqi zhang, Chang Yan, Xiaowen Chu, Tong-yi Zhang

Perovskite-LLM: Knowledge-Enhanced Large Language Models for Perovskite Solar Cell Research: 2 upvotes on Hugging Face Daily Papers, #30 of 33 papers on 2025-02-19. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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