中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 过程工程研究所
  3. 中国科学院过程工程研究所
Innovative Materials Science via Machine Learning

文献类型：期刊论文

作者Gao, Chaochao1; Min, Xin1; Fang, Minghao1; Tao, Tianyi2; Zheng, Xiaohong1,2; Liu, Yangai1; Wu, Xiaowen1; Huang, Zhaohui1
刊名ADVANCED FUNCTIONAL MATERIALS
出版日期2021-10-04
页码14
ISSN号1616-301X
关键词data-driven machine learning materials discovery materials science performances prediction perspective
DOI10.1002/adfm.202108044
英文摘要Nowadays, the research on materials science is rapidly entering a phase of data-driven age. Machine learning, one of the most powerful data-driven methods, have been being applied to materials discovery and performances prediction with undoubtedly tremendous application foreground. Herein, the challenges and current progress of machine learning are summarized in materials science, the design strategies are classified and highlighted, and possible perspectives are proposed for the future development. It is hoped this review can provide important scientific guidance for innovating materials science and technology via machine learning in the future.
WOS关键词DENSITY-FUNCTIONAL THEORY ; NEURAL-NETWORK ; DIMENSIONALITY REDUCTION ; THERMODYNAMIC STABILITY ; CRYSTAL-STRUCTURES ; DESIGN ; PERFORMANCE ; PREDICTION ; DATABASE ; SEARCH
资助项目National Key R&D Program of China[2018YFC1901500]
WOS研究方向Chemistry ; Science & Technology - Other Topics ; Materials Science ; Physics
语种英语
出版者WILEY-V C H VERLAG GMBH
WOS记录号WOS:000702995500001
资助机构National Key R&D Program of China
源URL[http://ir.ipe.ac.cn/handle/122111/50371]  
专题中国科学院过程工程研究所
通讯作者Min, Xin; Huang, Zhaohui
作者单位1.China Univ Geosci Beijing, Sch Mat Sci & Technol, Natl Lab Mineral Mat, Beijing Key Lab Mat Utilizat Nonmetall Minerals &, Beijing 100083, Peoples R China
2.Chinese Acad Sci, Inst Proc Engn, Div Environm Technol & Engn, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Gao, Chaochao,Min, Xin,Fang, Minghao,et al. Innovative Materials Science via Machine Learning[J]. ADVANCED FUNCTIONAL MATERIALS,2021:14.
APA Gao, Chaochao.,Min, Xin.,Fang, Minghao.,Tao, Tianyi.,Zheng, Xiaohong.,...&Huang, Zhaohui.(2021).Innovative Materials Science via Machine Learning.ADVANCED FUNCTIONAL MATERIALS,14.
MLA Gao, Chaochao,et al."Innovative Materials Science via Machine Learning".ADVANCED FUNCTIONAL MATERIALS (2021):14.

入库方式： OAI收割

来源：过程工程研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。