Home Journals Articles Updates About
CN
image
收藏切换
Research Progress on Data Science of Solid Oxide Fuel Cells, Lithium Batteries, CO2 Electroreduction Catalysts
收藏切换
Jianbing XU1, 2, Hanshi LI3, Jimin TAN4, Minfang HAN5, Di CHEN1, 6
Journal of the Chinese Ceramic Society | 2022, 50(11) : 3021 - 3039
Less
收藏切换
Journal of the Chinese Ceramic Society | 2022, 50(11): 3021-3039
• Review •
Research Progress on Data Science of Solid Oxide Fuel Cells, Lithium Batteries, CO2 Electroreduction Catalysts
Full
Jianbing XU1, 2, Hanshi LI3, Jimin TAN4, Minfang HAN5, Di CHEN1, 6
Affiliations
  • 1.The Future Laboratory, Tsinghua University, Beijing 100084, China
  • 2.Academy of Arts & Design, Tsinghua University, Beijing 100084, China
  • 3.Department of Mechanical Engineering and Materials Science, Yale University, New Haven 06520, CT, USA.
  • 4.Center for Data Science, New York University, New York 10012, NY, USA.
  • 5.Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, China
  • 6.The School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China

    • XU Jianbing (1991-), male, Ph.D. E-mail:

About Author:

XU Jianbing (1991-), male, Ph.D. E-mail:

Published: 2022-11-20 doi: 10.14062/j.issn.0454-5648.20220433
Outline
收藏切换

Lithium ion batteries and solid oxide fuel cells have become popular as clean energy devices. However, the commercial application of the batteries as complex electric power systems requires the batteries with long-term, multi-dimensional and high-precision performance. Some battery performance prediction methods are still in the initial stage of exploration. With the popularization and promotion of artificial intelligence, machine learning based on the artificial neural network technology has attracted recent attention. Recent advances in data science, such as machine learning, provide the scientific and engineering communities with flexible and rapid prediction frameworks, indicating great application prospects in materials research and development. This review summarized the latest advances on machine learning for the development of renewable energy technologies (i.e., solid oxide fuel cells, lithium batteries) and carbon reduction technologies, and gave some comments for the future development directions.

data science  /  machine learning  /  renewable energy  /  solid oxide fuel cells  /  lithium batteries  /  carbon dioxide electrochemical reduction catalysts
许建兵, 李翰实, 谭济民, 韩敏芳, 陈迪. 机器学习在固体氧化物燃料电池、锂电池、CO2电还原催化剂中的研究进展. 硅酸盐学报, 2022 , 50 (11) : 3021 -3039 . DOI: 10.14062/j.issn.0454-5648.20220433
Jianbing XU, Hanshi LI, Jimin TAN, Minfang HAN, Di CHEN. Research Progress on Data Science of Solid Oxide Fuel Cells, Lithium Batteries, CO2 Electroreduction Catalysts[J]. Journal of the Chinese Ceramic Society, 2022 , 50 (11) : 3021 -3039 . DOI: 10.14062/j.issn.0454-5648.20220433
Appendix
Less
Year 2022 volume 50 Issue 11
23
3
Cite this Article
BibTeX
Article Info
doi: 10.14062/j.issn.0454-5648.20220433
  • Receive Date：2022-05-28
  • Online Date：2026-10-20
  • Published：2022-11-20
Article Data
Affiliations
History
  • Received：2022-05-28
  • Revised：2022-06-30
Funding
Affiliations
    1.The Future Laboratory, Tsinghua University, Beijing 100084, China
    2.Academy of Arts & Design, Tsinghua University, Beijing 100084, China
    3.Department of Mechanical Engineering and Materials Science, Yale University, New Haven 06520, CT, USA.
    4.Center for Data Science, New York University, New York 10012, NY, USA.
    5.Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, China
    6.The School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China

Corresponding:

CHEN Di (1987-), male, Ph.D., Associate Professor. E-mail:
References
Share
https://castjournals.cast.org.cn/joweb/gsyxb/EN/10.14062/j.issn.0454-5648.20220433
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62662699(Operation and Technical Support)
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House