Large Language Models as Master Key: Unlocking the Secrets of Materials Science with GPT

This article presents a new NLP task called structured information inference (SII) to address the complexities of information extraction at the device level in materials science. We accomplished this task by tuning GPT-3 on an existed perovskite solar cell FAIR(Findable, Accessible, Interoperable, Reusable) dataset with 91.8 F1-score and we updated the dataset with all related scientific papers up to now. The produced dataset is formatted and normalized, enabling its direct utilization as input in subsequent data analysis. This feature will enable materials scientists to develop their own models by selecting high-quality review papers within their domain. Furthermore, we designed experiments to predict solar cells' electrical performance and reverse-predict parameters on both material gene and FAIR datesets through LLM. We obtained comparable performance with traditional machine learning methods without feature selection, which demonstrates the potential of large language models to judge materials and design new materials like a materials scientist.