Mining experimental data from materials science literature with large language models: an evaluation study-Reference-Cited by-同舟云学术

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Mining experimental data from materials science literature with large language models: an evaluation study

Published:2024-07-08 Issue:1 Volume:4 Page:
ISSN:2766-0400
Container-title:Science and Technology of Advanced Materials: Methods
language:en
Short-container-title:Science and Technology of Advanced Materials: Methods

Author:

Foppiano Luca¹²^ORCID,Lambard Guillaume³^ORCID,Amagasa Toshiyuki²,Ishii Masashi³^ORCID

Affiliation:

1. Materials Modeling Group, Center for Basic Research on Materials, National Institute for Materials Science, Tsukuba-shi, Japan

2. Knowledge and Data Engineering, Centre for Computational Sciences, University of Tsukuba, Tsukuba, Japan

3. Data-driven Materials Design Group, Center for Basic Research on Materials, National Institute for Materials Science, Tsukuba-shi, Japan

Funder

Research and Development

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/27660400.2024.2356506

Reference35 articles.

1. Small data machine learning in materials science

2. Data-driven design of metal–organic frameworks for wet flue gas CO2 capture

3. Machine learning–enabled high-entropy alloy discovery

4. An open experimental database for exploring inorganic materials

5. A polymer dataset for accelerated property prediction and design

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Hierarchical Context Augmentation Method to Improve Retrieval-Augmented LLMs on Scientific Papers;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

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