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

被引:0
|
作者
Xue Jiang [1 ]
Weiren Wang [2 ]
Shaohan Tian [1 ]
Hao Wang [1 ]
Turab Lookman [1 ]
Yanjing Su [3 ]
机构
[1] University of Science and Technology Beijing,Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology
[2] Liaoning Academy of Materials,undefined
[3] AiMaterials Research LLC,undefined
[4] Suzhou Laboratory,undefined
关键词
D O I
10.1038/s41524-025-01554-0
中图分类号
学科分类号
摘要
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.
引用
下载
收藏
相关论文
共 50 条
  • [1] Benchmarking large language models for biomedical natural language processing applications and recommendations
    Qingyu Chen
    Yan Hu
    Xueqing Peng
    Qianqian Xie
    Qiao Jin
    Aidan Gilson
    Maxwell B. Singer
    Xuguang Ai
    Po-Ting Lai
    Zhizheng Wang
    Vipina K. Keloth
    Kalpana Raja
    Jimin Huang
    Huan He
    Fongci Lin
    Jingcheng Du
    Rui Zhang
    W. Jim Zheng
    Ron A. Adelman
    Zhiyong Lu
    Hua Xu
    Nature Communications, 16 (1)
  • [2] Natural language processing in the era of large language models
    Zubiaga, Arkaitz
    FRONTIERS IN ARTIFICIAL INTELLIGENCE, 2024, 6
  • [3] Accelerating materials language processing with large language models
    Jaewoong Choi
    Byungju Lee
    Communications Materials, 5
  • [4] Accelerating materials language processing with large language models
    Choi, Jaewoong
    Lee, Byungju
    COMMUNICATIONS MATERIALS, 2024, 5 (01)
  • [5] Robustness of GPT Large Language Models on Natural Language Processing Tasks
    Xuanting C.
    Junjie Y.
    Can Z.
    Nuo X.
    Tao G.
    Qi Z.
    Jisuanji Yanjiu yu Fazhan/Computer Research and Development, 2024, 61 (05): : 1128 - 1142
  • [6] BioInstruct: instruction tuning of large language models for biomedical natural language processing
    Tran, Hieu
    Yang, Zhichao
    Yao, Zonghai
    Yu, Hong
    JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, 2024, 31 (09) : 1821 - 1832
  • [7] Research and Exploration on Chinese Natural Language Processing in Era of Large Language Models
    大模型时代下的汉语自然语言处理研究与探索
    Xi, Xuefeng (xfxi@mail.usts.edu.cn), 2025, 61 (01) : 80 - 97
  • [8] Large Language Models are Not Models of Natural Language: They are Corpus Models
    Veres, Csaba
    IEEE ACCESS, 2022, 10 : 61970 - 61979
  • [9] Applications of natural language processing
    Blandon Andrade, Juan Carlos
    ENTRE CIENCIA E INGENIERIA, 2022, 16 (31): : 7 - 8
  • [10] The journey from natural language processing to large language models: key insights for radiologists
    Salvatore Claudio Fanni
    Lorenzo Tumminello
    Valentina Formica
    Francesca Pia Caputo
    Gayane Aghakhanyan
    Ilaria Ambrosini
    Roberto Francischello
    Lorenzo Faggioni
    Dania Cioni
    Emanuele Neri
    Journal of Medical Imaging and Interventional Radiology, 11 (1):