Artificial intelligence in accelerating the development of air electrode materials for proton conducting solid oxide cells

Chunmei Tang; Fangyuan Zheng; Lei Zhao; Baoyin Yuan; Zhiyin Huang; Siyu Ye; Ning Wang

doi:10.54227/mlab.20230021

Article navigation > Materials Lab > 2024 > 3, 230021

Chunmei Tang, Fangyuan Zheng, Lei Zhao, Baoyin Yuan, Zhiyin Huang, Siyu Ye, Ning Wang. Artificial intelligence in accelerating the development of air electrode materials for proton conducting solid oxide cells[J]. Materials Lab, 2024, 3(2): 230021. doi: 10.54227/mlab.20230021

Citation:

Chunmei Tang, Fangyuan Zheng, Lei Zhao, Baoyin Yuan, Zhiyin Huang, Siyu Ye, Ning Wang. Artificial intelligence in accelerating the development of air electrode materials for proton conducting solid oxide cells[J]. Materials Lab, 2024, 3(2): 230021. doi: 10.54227/mlab.20230021

PERSPECTIVE

Artificial intelligence in accelerating the development of air electrode materials for proton conducting solid oxide cells

More Information

1.
Huangpu Hydrogen Energy Innovation Centre, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China
2.
Key Laboratory of High-precision Computation and Application of Quantum Field Theory of Hebei Province, College Physics Science and Technology, Hebei University Baoding 071002, China
3.
School of Mathematics, South China University of Technology, Guangzhou 510640, China
Corresponding authors: baoyinyuan@scut.edu.cn; ningwang@gzhu.edu.cn

Abstract

Abstract

Proton conducting solid oxide cells (P-SOCs) operating at intermediate temperature, working in both fuel cell mode for power generation and electrolysis mode for hydrogen production, gain much attention due to their unique advantages. However, the lack of efficient air electrode is the main obstacle to get high-performance P-SOCs, moreover, developing such materials relies on the high-cost and time-waste traditional way. The application of artificial intelligence (AI) to the field of P-SOCs can solve the problems that the traditional way faced. In this perspective, we discussed the current reports relating to the development of air electrode materials of P-SOC by constructing machine learning models. Finally, the future directions of AI guiding the discovery of key materials and high-performance P-SOCs are proposed.
- Proton conducting solid oxide cells /
- Air electrode /
- Machine learning
Information

Received Date: 27 April 2023

Revised Date: 17 June 2023

Accepted Date: 23 August 2023

Available Online: 16 September 2023

Publish Date: 12 July 2024

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Author Biographies

Author Biographies

Chunmei Tang received her Ph.D. from Graduate School of Chemical Sciences and Engineering, Hokkaido University, Japan in 2022. Then she joined in the group of Prof. S. Ye, China, as a postdoctoral and her focuses on protonic solid oxide electrolysis cells, protonic conductor, hydrogen production, and thermoelectric materials.

Ning Wang is an associate professor at Guangzhou University, China. She received her Ph.D. in Chemistry of Engineering from Hokkaido University, Japan in 2020. Her research mainly focuses on protonic conducting fuel cell, electrolysis cell, mixed ionic–electronic conductors, and machine learning.

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