mlsanityData to 14 Oct 2026

Perovskite-R1: a domain-specialized large language model for intelligent discovery of precursor additives and experimental design

arXiv 2507.16307 ↗First posted 22 Jul 2025cs.LG cond-mat.mtrl-sci cs.AIcited by 1

Not on the map. Why?

Abstract (arXiv)

Perovskite solar cells (PSCs) have rapidly emerged as a leading contender in next-generation photovoltaic technologies, owing to their exceptional power conversion efficiencies and advantageous material properties. Despite these advances, challenges such as long-term stability, environmental sustainability, and scalable manufacturing continue to hinder their commercialization. Precursor additive engineering has shown promise in addressing these issues by enhancing both the performance and durability of PSCs. However, the explosive growth of scientific literature and the complex interplay of materials, processes, and device architectures make it increasingly difficult for researchers to efficiently access, organize, and utilize domain knowledge in this rapidly evolving field. To address this gap, we introduce Perovskite-R1, a specialized large language model (LLM) with advanced reasoning capabilities tailored for the discovery and design of PSC precursor additives. By systematically mining and curating 1,232 high-quality scientific publications and integrating a comprehensive library of 33,269 candidate materials, we constructed a domain-specific instruction-tuning dataset using automated question-answer generation and chain-of-thought reasoning. Fine-tuning the QwQ-32B model on this dataset resulted in Perovskite-R1, which can intelligently synthesize literature insights and generate innovative and practical solutions for defect passivation and the selection of precursor additives. Experimental validation of several model-proposed strategies confirms their effectiveness in improving material stability and performance. Our work demonstrates the potential of domain-adapted LLMs in accelerating materials discovery and provides a closed-loop framework for intelligent, data-driven advancements in perovskite photovoltaic research.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 28 of its 59 references that are in the map. First posted on arXiv 22 Jul 2025.

Builds on

28 of 59 references.

References · 28

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  3. 2025Qwen3 Technical Reportcited by 8,820
  4. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  5. 2024GPT-4o System Cardcited by 4,588
  6. 2023Qwen Technical Reportcited by 3,344
  7. 2024LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Modelscited by 1,464
  8. 2021Crystal Diffusion Variational Autoencoder for Periodic Material Generationcited by 270
  9. 2023Crystal Structure Prediction by Joint Equivariant Diffusioncited by 148
  10. 2023BioMedGPT: Open Multimodal Generative Pre-trained Transformer for BioMedicinecited by 100
  11. 2024Fine-Tuned Language Models Generate Stable Inorganic Materials as Textcited by 93
  12. 2025Fin-R1: A Large Language Model for Financial Reasoning through Reinforcement Learningcited by 83
16 more
  1. 2023InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuningcited by 82
  2. 2024HoneyComb: A Flexible LLM-Based Agent System for Materials Sciencecited by 57
  3. 2024FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributionscited by 53
  4. 2024LLaMP: Large Language Model Made Powerful for High-fidelity Materials Knowledge Retrieval and Distillationcited by 32
  5. 2024Foundational Large Language Models for Materials Researchcited by 18
  6. 2025MatterChat: A Multi-Modal LLM for Material Sciencecited by 18
  7. 2024AI-driven inverse design of materials: Past, present and futurecited by 10
  8. 2025Materials Generation in the Era of Artificial Intelligence: A Comprehensive Surveycited by 10
  9. 2024CourseGPT-zh: an Educational Large Language Model Based on Knowledge Distillation Incorporating Prompt Optimizationcited by 5
  10. 2025AI-driven materials design: a mini-reviewcited by 5
  11. 2025MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generationcited by 5
  12. 2024PharmaGPT: Domain-Specific Large Language Models for Bio-Pharmaceutical and Chemistrycited by 4
  13. 2024Beyond Answers: Large Language Model-Powered Tutoring System in Physics Education for Deep Learning and Precise Understandingcited by 3
  14. 2024Financial Knowledge Large Language Modelcited by 3
  15. 2025Perovskite-LLM: Knowledge-Enhanced Large Language Models for Perovskite Solar Cell Researchcited by 2
  16. 2025Graph Neural Network Prediction of Nonlinear Optical Propertiescited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026LEAP: A closed-loop framework for perovskite precursor additive discoverycited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026PeroMAS: A Multi-agent System of Perovskite Material Discovery
  2. 2025Enhanced Conditional Generation of Double Perovskite by Knowledge-Guided Language Model Feedback
  3. 2025Role of Large Language Models and Retrieval-Augmented Generation for Accelerating Crystalline Material Discovery: A Systematic Review
  4. 2026LLM-Driven Discovery of High-Entropy Catalysts via Retrieval-Augmented Generation
  5. 2026MatMind: A Structure-Activity Knowledge-Driven Generative Foundation Model for Materials Science
  6. 2025Accelerating High-Efficiency Organic Photovoltaic Discovery via Pretrained Graph Neural Networks and Generative Reinforcement Learning
  7. 2025Autonomous Inorganic Materials Discovery via Multi-Agent Physics-Aware Scientific Reasoning
  8. 2026Chemical filters for ultra-high-throughput materials screening and generation