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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2405.15290 |
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| _version_ | 1866914948524802048 |
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| author | Sipilä, M. Mehryary, F. Pyysalo, S. Ginter, F. Todorović, Milica |
| author_facet | Sipilä, M. Mehryary, F. Pyysalo, S. Ginter, F. Todorović, Milica |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_15290 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Question Answering models for information extraction from perovskite materials science literature Sipilä, M. Mehryary, F. Pyysalo, S. Ginter, F. Todorović, Milica Materials Science 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. |
| title | Question Answering models for information extraction from perovskite materials science literature |
| topic | Materials Science |
| url | https://arxiv.org/abs/2405.15290 |