Data-Driven Materials Innovation and Applications

© 2022 Wiley-VCH GmbH.

Bibliographische Detailangaben
Veröffentlicht in:Advanced materials (Deerfield Beach, Fla.). - 1998. - 34(2022), 36 vom: 20. Sept., Seite e2104113
1. Verfasser: Wang, Zhuo (VerfasserIn)
Weitere Verfasser: Sun, Zhehao, Yin, Hang, Liu, Xinghui, Wang, Jinlan, Zhao, Haitao, Pang, Cheng Heng, Wu, Tao, Li, Shuzhou, Yin, Zongyou, Yu, Xue-Feng
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2022
Zugriff auf das übergeordnete Werk:Advanced materials (Deerfield Beach, Fla.)
Schlagworte:Journal Article Review data-driven research machine learning material applications material informatics material innovation
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520 |a Owing to the rapid developments to improve the accuracy and efficiency of both experimental and computational investigative methodologies, the massive amounts of data generated have led the field of materials science into the fourth paradigm of data-driven scientific research. This transition requires the development of authoritative and up-to-date frameworks for data-driven approaches for material innovation. A critical discussion on the current advances in the data-driven discovery of materials with a focus on frameworks, machine-learning algorithms, material-specific databases, descriptors, and targeted applications in the field of inorganic materials is presented. Frameworks for rationalizing data-driven material innovation are described, and a critical review of essential subdisciplines is presented, including: i) advanced data-intensive strategies and machine-learning algorithms; ii) material databases and related tools and platforms for data generation and management; iii) commonly used molecular descriptors used in data-driven processes. Furthermore, an in-depth discussion on the broad applications of material innovation, such as energy conversion and storage, environmental decontamination, flexible electronics, optoelectronics, superconductors, metallic glasses, and magnetic materials, is provided. Finally, how these subdisciplines (with insights into the synergy of materials science, computational tools, and mathematics) support data-driven paradigms is outlined, and the opportunities and challenges in data-driven material innovation are highlighted 
650 4 |a Journal Article 
650 4 |a Review 
650 4 |a data-driven research 
650 4 |a machine learning 
650 4 |a material applications 
650 4 |a material informatics 
650 4 |a material innovation 
700 1 |a Sun, Zhehao  |e verfasserin  |4 aut 
700 1 |a Yin, Hang  |e verfasserin  |4 aut 
700 1 |a Liu, Xinghui  |e verfasserin  |4 aut 
700 1 |a Wang, Jinlan  |e verfasserin  |4 aut 
700 1 |a Zhao, Haitao  |e verfasserin  |4 aut 
700 1 |a Pang, Cheng Heng  |e verfasserin  |4 aut 
700 1 |a Wu, Tao  |e verfasserin  |4 aut 
700 1 |a Li, Shuzhou  |e verfasserin  |4 aut 
700 1 |a Yin, Zongyou  |e verfasserin  |4 aut 
700 1 |a Yu, Xue-Feng  |e verfasserin  |4 aut 
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773 1 8 |g volume:34  |g year:2022  |g number:36  |g day:20  |g month:09  |g pages:e2104113 
856 4 0 |u http://dx.doi.org/10.1002/adma.202104113  |3 Volltext 
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