  The amount of data has growing significance in exploring cutting-edge
materials and a number of datasets have been generated either by hand or
automated approaches. However, the materials science field struggles to
effectively utilize the abundance of data, especially in applied disciplines
where materials are evaluated based on device performance rather than their
properties. This article presents a new natural language processing (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 existing perovskite solar cell
FAIR (Findable, Accessible, Interoperable, Reusable) dataset with 91.8%
F1-score and extended the dataset with data published since its release. The
produced data is formatted and normalized, enabling its direct utilization as
input in subsequent data analysis. This feature empowers materials scientists
to develop models by selecting high-quality review articles within their
domain. Additionally, we designed experiments to predict the electrical
performance of solar cells and design materials or devices with targeted
parameters using large language models (LLMs). Our results demonstrate
comparable performance to traditional machine learning methods without feature
selection, highlighting the potential of LLMs to acquire scientific knowledge
and design new materials akin to materials scientists.
