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

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.

Automated summary

There is a growing demand for innovative materials in the development of new industries. To overcome the laborious and time-consuming process of locating such materials, researchers are shifting towards using existing material science research knowledge more efficiently. Natural language processing (NLP) has emerged as a crucial technology in this movement, proving to be valuable for processing language-based data in materials science literature.