@inproceedings{liu-etal-2025-perovskite,
title = "Perovskite-{LLM}: Knowledge-Enhanced Large Language Models for Perovskite Solar Cell Research",
author = "Liu, Xiang and
Sun, Penglei and
Chen, Shuyan and
Zhang, Longhan and
Dong, Peijie and
You, Huajie and
Zhang, Yongqi and
Yan, Chang and
Chu, Xiaowen and
Zhang, Tong-yi",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.27/",
doi = "10.18653/v1/2025.findings-emnlp.27",
pages = "494--518",
ISBN = "979-8-89176-335-7",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Perovskite-LLM: Knowledge-Enhanced Large Language Models for Perovskite Solar Cell Research
%A Liu, Xiang
%A Sun, Penglei
%A Chen, Shuyan
%A Zhang, Longhan
%A Dong, Peijie
%A You, Huajie
%A Zhang, Yongqi
%A Yan, Chang
%A Chu, Xiaowen
%A Zhang, Tong-yi
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F liu-etal-2025-perovskite
%X 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.
%R 10.18653/v1/2025.findings-emnlp.27
%U https://aclanthology.org/2025.findings-emnlp.27/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.27
%P 494-518
Markdown (Informal)
[Perovskite-LLM: Knowledge-Enhanced Large Language Models for Perovskite Solar Cell Research](https://aclanthology.org/2025.findings-emnlp.27/) (Liu et al., Findings 2025)
ACL
- Xiang Liu, Penglei Sun, Shuyan Chen, Longhan Zhang, Peijie Dong, Huajie You, Yongqi Zhang, Chang Yan, Xiaowen Chu, and Tong-yi Zhang. 2025. Perovskite-LLM: Knowledge-Enhanced Large Language Models for Perovskite Solar Cell Research. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 494–518, Suzhou, China. Association for Computational Linguistics.