Data-driven materials innovation and applications
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 requir...
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sg-ntu-dr.10356-1634652023-07-14T16:07:14Z Data-driven materials innovation and applications Wang, Zhuo Sun, Zhehao Yin, Hang Liu, Xinghui Wang, Jinlan Zhao, Haitao Pang, Cheng Heng Wu, Tao Li, Shuzhou Yin, Zongyou Yu, Xue-Feng School of Materials Science and Engineering Engineering::Materials Machine Learning Material Applications 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. Submitted/Accepted version Z.W., Z.S., H.Y., and X.L. contributed equally to this work. The authors acknowledge the support from the National Natural Science Foundation of China (52173234) and ANU Futures Scheme (Q4601024). The authors also gratefully express gratitude to all parties which have contributed toward the success of this project, both financially and technically, especially the S&T Innovation 2025 Major Special Programme (2018B10022) and Commonwealth Programme (2022S122) funded by the Ningbo Science and Technology Bureau, China, as well as the Provincial Key Laboratory Programme (2020E10018) funded by the Zhejiang Provincial Department of Science and Technology and the support from Ningbo Municipal Key Laboratory on Clean Energy Conversion Technologies (2014A22010). The authors also appreciate the support from the Shenzhen Science and Technology Program (JCY20210324102008023), Shenzhen-Hong Kong-Macau Technology Research Program (Type C, SGDX2020110309300301), Natural Science Foundation of Guangdong Province (2022A1515010554), and CCFTencent Open Fund. 2022-12-07T03:13:20Z 2022-12-07T03:13:20Z 2022 Journal Article Wang, Z., Sun, Z., Yin, H., Liu, X., Wang, J., Zhao, H., Pang, C. H., Wu, T., Li, S., Yin, Z. & Yu, X. (2022). Data-driven materials innovation and applications. Advanced Materials, 34(36), 2104113-. https://dx.doi.org/10.1002/adma.202104113 0935-9648 https://hdl.handle.net/10356/163465 10.1002/adma.202104113 35451528 2-s2.0-85135176418 36 34 2104113 en Advanced Materials © 2022 Wiley-VCH GmbH. All rights reserved. This is the peer reviewed version of the following article: Wang, Z., Sun, Z., Yin, H., Liu, X., Wang, J., Zhao, H., Pang, C. H., Wu, T., Li, S., Yin, Z. & Yu, X. (2022). Data-driven materials innovation and applications. Advanced Materials, 34(36), 2104113-, which has been published in final form at https://doi.org/10.1002/adma.202104113. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. application/pdf |
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Engineering::Materials Machine Learning Material Applications Wang, Zhuo Sun, Zhehao Yin, Hang Liu, Xinghui Wang, Jinlan Zhao, Haitao Pang, Cheng Heng Wu, Tao Li, Shuzhou Yin, Zongyou Yu, Xue-Feng Data-driven materials innovation and applications |
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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. |
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School of Materials Science and Engineering |
author_facet |
School of Materials Science and Engineering Wang, Zhuo Sun, Zhehao Yin, Hang Liu, Xinghui Wang, Jinlan Zhao, Haitao Pang, Cheng Heng Wu, Tao Li, Shuzhou Yin, Zongyou Yu, Xue-Feng |
format |
Article |
author |
Wang, Zhuo Sun, Zhehao Yin, Hang Liu, Xinghui Wang, Jinlan Zhao, Haitao Pang, Cheng Heng Wu, Tao Li, Shuzhou Yin, Zongyou Yu, Xue-Feng |
author_sort |
Wang, Zhuo |
title |
Data-driven materials innovation and applications |
title_short |
Data-driven materials innovation and applications |
title_full |
Data-driven materials innovation and applications |
title_fullStr |
Data-driven materials innovation and applications |
title_full_unstemmed |
Data-driven materials innovation and applications |
title_sort |
data-driven materials innovation and applications |
publishDate |
2022 |
url |
https://hdl.handle.net/10356/163465 |
_version_ |
1773551382969712640 |