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Natural Language Processing Techniques for Advancing Materials Discovery: A Short Review

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Abstract

In the development of new industries, there is a growing demand for innovative materials. However, locating such materials is a laborious and time-consuming endeavor. In response, there has been a shift toward studying new materials more efficiently using existing material science research knowledge. There has been an increase in the number of materials science-related papers over the past two decades, and attempts to use them for research purposes have increased as the methods have been systematized. Past research papers, for instance, can be used to predict new materials or obtain optimal synthesis parameters for materials with the desired properties. In this movement, natural language processing (NLP) is a crucial technology. In the past decade, NLP has emerged as one of the most rapidly expanding areas of artificial intelligence, proving to be a valuable tool for processing language-based data. In this review, we will examine how NLP is used in the materials science literature, what processes it can be used for, and the primary NLP technologies currently in use, with a particular focus on specific use cases. We will also discuss this approach's limitations.

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2022R1F1A1074339, No. 2022R1C1C1009387).

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  1. Joo Hyuk Lee and Myeonghun Lee have equally contributed to this work.

Authors and Affiliations

  1. School of Mechanical Engineering, Soongsil University, 369 Sangdo-Ro, Dongjak-gu, Seoul, 06978, Republic of Korea

    Joo Hyuk Lee & Kyoungmin Min

  2. School of Systems Biomedical Science, Soongsil University, 369 Sangdo-Ro, Dongjak-gu, Seoul, 06978, Republic of Korea

    Myeonghun Lee

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Lee, J.H., Lee, M. & Min, K. Natural Language Processing Techniques for Advancing Materials Discovery: A Short Review. Int. J. of Precis. Eng. and Manuf.-Green Tech. 10, 1337–1349 (2023). https://doi.org/10.1007/s40684-023-00523-6

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