Question Answering models for information extraction from perovskite materials science literature

TL;DR

This paper introduces a QA-based approach for extracting material-property relationships from scientific literature, demonstrating improved accuracy in identifying perovskite bandgaps and highlighting the method's potential for materials discovery.

Contribution

The study presents a novel QA workflow using fine-tuned large language models, achieving state-of-the-art accuracy in extracting material properties from scientific texts.

Findings

QA MatBERT achieved highest extraction accuracy

F1-scores surpassed current state-of-the-art

QA approach shows high versatility and potential for materials research

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 novel approach to extract material-property relationships from scientific publications using the Question Answering (QA) method. 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 MatBERT and F1-scores improved on the current state-of-the-art. This work demonstrates the QA workflow and paves the way towards further applications. The simplicity, versatility and accuracy of the QA approach all point to its considerable potential for text-driven discoveries in materials research.