# Question Answering models for information extraction from perovskite materials science literature

Source: [Question Answering models for information extraction from perovskite materials science literature](<https://www.sota2.com/research/paper/68fa47fe1e209131ce92c489>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:54:49.588Z

Authors: M. Sipil\\"a, F. Mehryary, S. Pyysalo, F. Ginter, Milica Todorovi\\'c

Published: 2024-05-24T07:24:21.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2405.15290>)

[PDF](<https://arxiv.org/pdf/2405.15290.pdf>)