Question Answering models for information extraction from perovskite materials science literature

Abstract Scientific text is a promising source of data in materials science, with ongoing research into utilising textual data for materials discovery. In this study, we developed and tested a Question Answering (QA) approach to extract material-property relationships from scientific publications. QA performance was evaluated for information extraction of perovskite bandgaps based on a human query. We observed considerable variation in results with five different large language models fine-tuned for the QA task. Best extraction accuracy was achieved with the QA MatSciBERT and F1-scores improved on the current state-of-the-art. QA also outperformed three latest generative large language models on the information extraction task, except the GPT-4 model. This work demonstrates the QA workflow and paves the way towards further applications. The simplicity and versatility of the QA approach all point to its considerable potential for text-driven discoveries in materials research.