Perovskite-LLM: Knowledge-Enhanced Large Language Models for Perovskite Solar Cell Research
This work addresses the need for efficient knowledge management and reasoning tools for researchers in perovskite solar cell research, though it is incremental as it applies existing methods to a new domain.
The authors tackled the problem of managing and reasoning with the rapidly growing literature on perovskite solar cells by developing a knowledge-enhanced system, which includes a knowledge graph, datasets, and specialized large language models, and demonstrated significant performance improvements over existing models in domain-specific tasks.
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.