ArticlePatterns (New York, N.Y.)2024
Creation of a structured solar cell material dataset and performance prediction using large language models.
Article in Patterns (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Large Language Model Guided Discovery of Hole Transport Layer Dopants for Efficient and Stable Perovskite Photovoltaics.Small methods · 2026Article
- FePTP: A text-mined dataset of transformation pathways among iron-containing phases.Scientific data · 2026Article
- Density Estimation Based on Mixtures of Gaussians for Perovskite Solar Cells Modeling.Journal of chemical information and modeling · 2026Article
- FoodSky: A food-oriented large language model that can pass the chef and dietetic examinations.Patterns (New York, N.Y.) · 2025Article
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Authors and funding
11 authors.
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Abstract
Materials scientists usually collect experimental data to summarize experiences and predict improved materials. However, a crucial issue is how to proficiently utilize unstructured data to update existing structured data, particularly in applied disciplines. This study introduces a new natural language processing (NLP) task called structured information inference (SII) to address this problem. We propose an end-to-end approach to summarize and organize the multi-layered device-level information from the literature into structured data. After comparing different methods, we fine-tuned LLaMA with an F1 score of 87.14% to update an existing perovskite solar cell dataset with articles published since its release, allowing its direct use in subsequent data analysis. Using structured information, we developed regression tasks to predict the electrical performance of solar cells. Our results demonstrate comparable performance to traditional machine-learning methods without feature selection and highlight the potential of large language models for scientific knowledge acquisition and material development.
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Registered trials
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