Perovskite- LLM : Knowledge-Enhanced Large Language Models for Perovskite Solar Cell Research (2025)

Abstract: 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.

## introduction perovskite solar cells (pscs) have emerged as one of the most promising next-generation photovoltaic technologies, achieving remarkable progress with power conversion efficiencies (pces) exceeding 27.0% within just over a decade @xcite @xcite @xcite @xcite . the rapid development of pscs has generated an exponential growth in research publications, making it increasingly challenging for researchers to efficiently access and utilize the vast amount of knowledge in this field. this challenge is particularly acute given the complex interplay between material composition, fabrication processes, and device structure that characterizes psc research. traditional approaches to scientific knowledge management, such as literature reviews and databases, while valuable, are limited in their ability to capture the intricate relationships between different aspects of psc research @xcite . furthermore, existing artificial intelligence systems in materials science typically focus on specific prediction tasks or general scientific knowledge, lacking the specialized capability to handle the unique characteristics of the research of perovskite solar cells and more general materials science research @xcite @xcite @xcite . this gap highlights the need for an integrated system that can both systematically organize domain knowledge and provide intelligent assistance to researchers. to address these challenges, we present a comprehensive knowledge-enhanced system specifically designed for the perovskite solar cell domain, consisting of three key components. first, we develop perovskite-kg, a domain-specific knowledge graph constructed from 1,517 research articles, containing 23,789 entities and 22,272 relationships between manufacturing processes, parameters, and performance metrics. second, we create a multi-agent framework for generating high-quality instruction-tuning data, which not only reduces annotation costs but also ensures high reliability and low hallucination through the synergy of multiple specialized agents and expert guidance. this framework generates two complementary datasets: (1) perovskite-chat, an instruction-tuning dataset comprising 55,101 high-quality question-answer pairs generated from 2,214 high-impact papers using a novel multi-agent framework, and (2) perovskite-reasoning, a collection of 2,217 carefully curated materials science problems designed to enhance scientific reasoning capabilities. third, we introduce two specialized large language models: perovskite-chat-llm for domain-specific knowledge assistance and perovskite-reasoning-llm for tackling complex materials science reasoning tasks. our work makes the following key contributions: ❶ construction of a comprehensive perovskite knowledge graph. we construct the first comprehensive knowledge graph for perovskite solar cells, organizing domain knowledge into a structured format that captures the relationships between materials, processes, and device performance. ## perovskite-kg to extract the potential domain knowledge k, we utilize a pre-trained large language model (llm), expressed as llm(•; θ), under a zero-shot setting where the parameters θ remain fixed. the whole pipeline can be formulated as below: after extracting knowledge, we conduct quality control procedures to ensure accuracy and reliability. these procedures include entity disambiguation and relationship deduplication. entity disambiguation in a knowledge graph aims to resolve ambiguity by identifying the unique entity that corresponds to an ambiguous mention, denoted as e mention , within a subgraph. the objective is to determine a distinct entity e * that accurately represents e mention . relationship deduplication involves identifying and merging redundant relations in the knowledge graph. for instance, given two relations r @xmath0, r, e 2 ) and r @xmath1, r ′ , e ′ 2 ), if they convey the same semantic meaning-that is, if (e 1 , e 2 ) and (e ′ ## instruction tuning dataset generation in this section, we collect 2, 214 the top level publications papers in the perovskite domain and design the instruction tuning dataset including question answering and multiple choice questions, containing 55, 101 instances around 4.4 million tokens, named perovskite-chat. our experiments show that our perovskite instruction tuning dataset can effectively improve the performance of llms on perovskite related tasks. figure 1 illustrates this multi-agent framework for the generation of instruction tuning dataset. the process begins with expert guidance and academic literature from various sources (including science, nature, elsevier, springer, arxiv, and others) as input. expert guidance is provided by the domain expert focused on 7 research categories, 21 research questions. let @xmath2, ..., d n } represent the collection of academic literature from various sources, and @xmath3, ..., c 7 } denote the expert guidance categories with the corresponding research questions @xmath4, ..., q 21 }. the multi-agent framework processes these inputs through three specialized agents: information extraction: the final instruction tuning dataset d is constructed as: next, we introduce perovskite-reasoning, a collection of 2,217 high-quality questions from materials science textbooks, designed to enhance reasoning capabilities in the perovskite and materials science domains. the questions were sourced from hundreds of widely used materials science and engineering textbooks, with a focus on perovskite solar cells and fundamental materials science concepts. our rigorous selection process applied three key criteria: clarity of problem statements, completeness of solution, and alignment with core materials science principles. materials science professors conducted expert assessments to categorize questions by difficulty level, validated through student performance data and baseline model testing. to develop comprehensive solution paths, we employed advanced language models like deepseek-r1 @xcite and o1 (openai, 2024) in a multi-step reasoning approach. this methodology involved decomposing complex problems into logical steps, applying key physical and chemical principles, and implementing systematic solution strategies with result validation. the resulting dataset features detailed reasoning chains that demonstrate step-bystep problem-solving processes, making it valuable for training models in scientific reasoning and materials science problem-solving. figure 2 (b) displays a wordcloud visualization of the dataset's most frequent terms, with "perovskite solar" and "solar cell" appearing as predominant phrases, reflecting the dataset's core focus. other frequently occurring terms such as "device structure," "configuration," and "stability" underscore the key technical aspects addressed. this distribution demonstrates the dataset's strong emphasis on device structural aspects, with performance-related queries forming the second largest category. ## conclusion in this work, we present a comprehensive knowledge-enhanced system for perovskite solar cell research, integrating three key components: perovskite-chat-llm: ... for the hole transport layer (htl), materials like pedot:pss are commonly used. these materials are spin-coated onto the substrate and annealed at a temperature of 150°c for 15 minutes. the perovskite precursor solution is then prepared using a mixed cation and halide composition, and spin-coated onto the htl. the spin-coating process involves a twostep procedure: first at 1,000 rpm for 10 seconds, followed by 6,000 rpm for 30 seconds, with anisole added during the second step to facilitate crystallization. ...