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Innovative Materials Science via Machine Learning.

Authors :
Gao, Chaochao
Min, Xin
Fang, Minghao
Tao, Tianyi
Zheng, Xiaohong
Liu, Yangai
Wu, Xiaowen
Huang, Zhaohui
Source :
Advanced Functional Materials; Jan2022, Vol. 32 Issue 1, p1-14, 14p
Publication Year :
2022

Abstract

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. [ABSTRACT FROM AUTHOR]

Subjects

Subjects :
MACHINE learning
MATERIALS science

Details

Language :
English
ISSN :
1616301X
Volume :
32
Issue :
1
Database :
Complementary Index
Journal :
Advanced Functional Materials
Publication Type :
Academic Journal
Accession number :
154484107
Full Text :
https://doi.org/10.1002/adfm.202108044