Applications of natural language processing and large language models in materials discovery

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
Xue Jiang, Weiren Wang, Shaohan Tian, Hao Wang, Turab Lookman, Yanjing Su
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引用次数: 0

Abstract

The transformative impact of artificial intelligence (AI) technologies on materials science has revolutionized the study of materials problems. By leveraging well-characterized datasets derived from the scientific literature, AI-powered tools such as Natural Language Processing (NLP) have opened new avenues to accelerate materials research. The advances in NLP techniques and the development of large language models (LLMs) facilitate the efficient extraction and utilization of information. This review explores the application of NLP tools in materials science, focusing on automatic data extraction, materials discovery, and autonomous research. We also discuss the challenges and opportunities associated with utilizing LLMs and outline the prospects and advancements that will propel the field forward.

Abstract Image

自然语言处理和大语言模型在材料发现中的应用
人工智能(AI)技术对材料科学的变革性影响已经彻底改变了材料问题的研究。通过利用来自科学文献的特征良好的数据集,自然语言处理(NLP)等人工智能工具为加速材料研究开辟了新的途径。自然语言处理技术的进步和大型语言模型的发展促进了信息的高效提取和利用。本文综述了NLP工具在材料科学中的应用,重点介绍了自动数据提取、材料发现和自主研究。我们还讨论了与利用法学硕士相关的挑战和机遇,并概述了将推动该领域向前发展的前景和进步。
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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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