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化工新型材料  2022, Vol. 50 Issue (9): 42-46    DOI: 10.19817/j.cnki.issn1006-3536.2022.09.009
  综述与专论 本期目录 | 过刊浏览 | 高级检索 |
材料科学中机器学习算法的应用综述
刘霏凝, 石竞琛, 王文杰, 赵瑞*
吉林师范大学计算机学院,四平136000
Review of machine learning algorithm applied in materials science
Liu Feining, Shi Jingchen, Wang Wenjie, Zhao Rui
Computer College of Jilin Normal University,Siping 136000
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摘要 材料信息学利用以数据为中心的方法促进材料科学的发展,从现有的材料数据库中提取材料特征,使用机器学习进行新材料的预测和研究。从机器学习方法(有监督学习、无监督学习、主动学习和半监督学习)的角度,总结了各种学习方法在性能预测、结构预测和算法优化方面的相关研究进展,并对机器学习在材料科学研究中还未解决的问题和未来可能的发展热点进行了分析与展望。
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刘霏凝
石竞琛
王文杰
赵瑞
关键词:  材料科学  机器学习  学习方法  性能预测  结构预测  算法优化    
Abstract: Materials informatics promotes the development of materials science by using data centric method.It extracts material features from existing material databases and uses machine learning to predict and research new materials.From the perspective of machine learning methods (supervised learning,unsupervised learning,active learning and semi supervised learning),the related research progress of various learning methods in performance prediction,structure prediction and algorithm optimization was summarized.The unsolved problems and possible future development hotspots of machine learning in materials science were analyzed and prospected.
Key words:  material science    machine learning    learning method    performance prediction    structure prediction    algorithm optimization
收稿日期:  2021-04-27      修回日期:  2022-05-14           出版日期:  2022-09-20      发布日期:  2022-09-27     
ZTFLH:  TP181  
基金资助: 国家自然科学基金(22078124)
通讯作者:  赵瑞(1975-),女,教授,从事低维纳米材料的光催化性能研究工作,E-mail:lijiatong_zr@163.com。   
作者简介:  刘霏凝(1997-),女,硕士研究生,主要从事基于机器学习的半导体材料带隙预测研究工作。
引用本文:    
刘霏凝, 石竞琛, 王文杰, 赵瑞. 材料科学中机器学习算法的应用综述[J]. 化工新型材料, 2022, 50(9): 42-46.
Liu Feining, Shi Jingchen, Wang Wenjie, Zhao Rui. Review of machine learning algorithm applied in materials science. New Chemical Materials, 2022, 50(9): 42-46.
链接本文:  
https://www.hgxx.org/CN/10.19817/j.cnki.issn1006-3536.2022.09.009  或          https://www.hgxx.org/CN/Y2022/V50/I9/42
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