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 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.
Cite
@article{arxiv.2405.15290,
title = {Question Answering models for information extraction from perovskite materials science literature},
author = {M. Sipilä and F. Mehryary and S. Pyysalo and F. Ginter and Milica Todorović},
journal= {arXiv preprint arXiv:2405.15290},
year = {2024}
}
Comments
The following article has been submitted to npj Computational Materials