This article presents a new NLP task called structured information inference
(SIS) to address the complexities of information extraction at the device level
in materials science. We accomplished this task by finetuning GPT-3 on a
exsiting perovskite solar cell FAIR 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 PCE and reverse-predict
parameters and obtained comparable performance with DFT, which demonstrates the
potential of large language models to judge materials and design new materials
like a materials scientist.

本文提出了一个新的 NLP 任务叫做结构化信息推理（SIS），通过在现有钙钛矿太阳能电池数据集上微调 GPT-3 得到 91.8 F1 分数，并更新了该数据集，使得其中的数据可以被材料科学家直接用于后续数据分析中，同时证明了大型语言模型可以像材料科学家一样判断材料并设计新材料。