Issue

Vol. 17 (2025)

Machine Learning Tailored Anodes for Efficient Hydrogen Energy Generation in Proton-Conducting Solid Oxide Electrolysis Cells

https://doi.org/10.1007/s40820-025-01764-7
Fangyuan Zheng
Huangpu Hydrogen Energy Innovation Center, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, People’s Republic of China
Baoyin Yuan
School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, People’s Republic of China
Youfeng Cai
Huangpu Hydrogen Energy Innovation Center, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, People’s Republic of China
Huanxin Xiang
Huangpu Hydrogen Energy Innovation Center, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, People’s Republic of China
Chunmei Tang
Huangpu Hydrogen Energy Innovation Center, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, People’s Republic of China
Ling Meng
Huangpu Hydrogen Energy Innovation Center, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, People’s Republic of China
Lei Du
Huangpu Hydrogen Energy Innovation Center, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, People’s Republic of China
Xiting Zhang
Huangpu Hydrogen Energy Innovation Center, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, People’s Republic of China
Feng Jiao
School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, People’s Republic of China
Yoshitaka Aoki
Faculty of Engineering, Hokkaido University, N13W8, Kita‑Ku, Sapporo 060‑8628, Japan
Ning Wang
Huangpu Hydrogen Energy Innovation Center, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, People’s Republic of China
Siyu Ye
Huangpu Hydrogen Energy Innovation Center, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, People’s Republic of China

Corresponding Author: Siyu Ye

siyu.ye@gzhu.edu.cn

Nano-Micro Letters, Vol. 17 (2025), Article Number: 274

Abstract

In the global trend of vigorously developing hydrogen energy, proton-conducting solid oxide electrolysis cells (P-SOECs) have attracted significant attention due to their advantages of high efficiency and not requiring precious metals. However, the application of P-SOECs faces challenges, particularly in developing high-performance anodes possessing both high catalytic activity and ionic conductivity. In this study, La0.9Ba0.1Co0.7Ni0.3O3−δ (LBCN9173) and La0.9Ca0.1Co0.7Ni0.3O3−δ (LCCN9173) oxides are tailored as promising anodes by machine learning model, achieving the synergistic enhancement of water oxidation reaction kinetics and proton conduction, which is confirmed by comprehensively analyzing experiment and density functional theory calculation results. Furthermore, the anodic reaction mechanisms for P-SOECs with these anodes are elucidated by analyzing distribution of relaxation time spectra and Gibbs energy of water oxidation reaction, manifesting that the dissociation of H2O is facilitated on LBCN9173 anode. As a result, P-SOEC with LBCN9173 anode demonstrates a top-rank current density of 2.45 A cm−2 at 1.3 V and an extremely low polarization resistance of 0.05 Ω cm2 at 650 °C. This multi-scale, multi-faceted research approach not only discovered a high-performance anode but also proved the robust framework for the machine learning-assisted design of anodes for P-SOECs.


Highlights:
1 Machine learning technique was employed to develop anode for proton-conducting solid oxide electrolysis cells (P-SOEC).
2 The screened high-performance La0.9Ba0.1Co0.7Ni0.3O3−δ (LBCN9173) and La0.9Ca0.1Co0.7Ni0.3O3−δ (LCCN9173) anodes achieved a synergistic enhancement of water oxidation reaction kinetics and proton-conducting ability.
3 P-SOECs with LBCN9173 anode demonstrated a top-rank current density of 2.45 A cm−2 and an extremely low polarization resistance of 0.05 Ω cm2 at 650 °C.

Keywords

Machine learning Proton-conducting solid oxide electrolysis cells Hydrogen energy Anode
Zheng, Fangyuan, Baoyin Yuan, Youfeng Cai, Huanxin Xiang, Chunmei Tang, Ling Meng, Lei Du, Xiting Zhang, Feng Jiao, Yoshitaka Aoki, Ning Wang, and Siyu Ye. 2025. “Machine Learning Tailored Anodes for Efficient Hydrogen Energy Generation in Proton-Conducting Solid Oxide Electrolysis Cells”. Nano-Micro Letters 17 (May):274. https://doi.org/10.1007/s40820-025-01764-7.
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