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Semantic Scholar Open Access 2021 157 sitasi

Innovative Materials Science via Machine Learning

Chao Gao X. Min M. Fang Tianyi Tao Xiaohong Zheng +3 lainnya
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Abstrak

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.

Topik & Kata Kunci

Materials Science

Penulis (8)

C

Chao Gao

X

X. Min

M

M. Fang

T

Tianyi Tao

X

Xiaohong Zheng

Y

Yan’gai Liu

X

Xiaowen Wu

Z

Zhaohui Huang

Format Sitasi

APA MLA BibTeX
Gao, C., Min, X., Fang, M., Tao, T., Zheng, X., Liu, Y. et al. (2021). Innovative Materials Science via Machine Learning. https://doi.org/10.1002/adfm.202108044

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Informasi Jurnal
Tahun Terbit
2021
Bahasa
en
Total Sitasi
157×
Sumber Database
Semantic Scholar
DOI
10.1002/adfm.202108044
Akses
Open Access ✓