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

作     者：Lee, Joo Hyuk Lee, Myeonghun Min, Kyoungmin 

作者机构：Soongsil Univ Sch Mech Engn 369 Sangdo Ro Seoul 06978 South Korea Soongsil Univ Sch Syst Biomed Sci 369 Sangdo Ro Seoul 06978 South Korea 

出 版 物：《INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING-GREEN TECHNOLOGY》 (Int. J. Precis. Eng. Manuf. Green Technol.)

年 卷 期：2023年第10卷第5期

页      面：1337-1349页

核心收录：

学科分类：08[工学] 0802[工学-机械工程] 

基　　金：National Research Foundation of Korea (NRF) - Korea government (MSIT) [2022R1F1A1074339  2022R1C1C1009387] 

主　　题：Natural language processing Text mining Information extraction Materials discovery 

摘      要：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.