Optimizing data extraction from materials science literature: a study of tools using large language models
Open accessAbstract
Benchmarking five AI tools on materials science literature shows promising capabilities, but performance remains inadequate for large-scale data extraction. Our analysis offers detailed insight and guidance for future methodological improvements.
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Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works
Detecting hallucinations in large language models using semantic entropy
Farquhar, S., Kossen, J., Kuhn, L., & Gal, Y. (2024) · Nature · doi:10.1038/s41586-024-07421-0 · Cited by 934 · see citing works
Large language models for generative information extraction: a survey
Xu, D., Chen, W., Peng, W., Zhang, C., Xu, T., Zhao, X., Wu, X., Zheng, Y., Wang, Y., & Chen, E. (2024) · Frontiers of Computer Science · doi:10.1007/s11704-024-40555-y · Cited by 326 · see citing works
Data extraction methods for systematic review (semi)automation: Update of a living systematic review
Schmidt, L., Finnerty Mutlu, A. N., Elmore, R., Olorisade, B. K., Thomas, J., & Higgins, J. P. T. (2025) · F1000Research · doi:10.12688/f1000research.51117.3 · Cited by 27 · see citing works
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- Authors
- Ning, W., Li, M., Reimers, J. R., & Kobayashi, R.
- Affiliations
- Shanghai University (CN)
- Australian National University (AU)
- Venue
- Digital Discovery
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2026
- Volume
- 5
- Issue
- 2
- Pages
- 698-715
- ISSN
- 2635-098X (ISSN-L)
- OpenAlex
- W4417187428
- Cited by
- 8
- References
- 4
- Citations by year
- 2026: 8
- FWCI
- 1.70
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Digital Discovery (publishedVersion, cc-by-nc)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- National Natural Science Foundation of China (12347164, 12404276, 2024T170541, GZC20231535)
- Australian Research Council (CE230100021)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Keywords
- data extraction, materials science literature, large language models, literature mining, research data extraction
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