Citation: Zhongyin Zhao, Yunfan Fu, Sihui Chen, Zhenye Liang, Shaoru Cheng, Xueshan Hu, Yunchao Yin, Jinlong Yang, Yang Liu, Jiayu Wan. Exploring the composition space of quinary metal oxides for oxygen evolution reaction on an automated platform[J]. Chinese Chemical Letters, ;2026, 37(6): 111829. doi: 10.1016/j.cclet.2025.111829 shu

Exploring the composition space of quinary metal oxides for oxygen evolution reaction on an automated platform

Zhongyin Zhao ^{a, b} ,
Yunfan Fu ^c ,
Sihui Chen ^c ,
Zhenye Liang ^b ,
Shaoru Cheng ^c ,
Xueshan Hu ^b ,
Yunchao Yin ^a ,
Jinlong Yang ^{a, *,} ,
Yang Liu ^{d, *,} ,
Jiayu Wan ^{b, *,}

a.
Guangdong Provincial Key Laboratory of New Energy Materials Service Safety, Shenzhen Key Laboratory of Energy Electrocatalytic Materials, College of Materials Science and Engineering, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
b.
Future Battery Research Center, Global Institute of Future Technology, Shanghai Jiaotong University, Shanghai 200240, China
c.
Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen 518055, China
d.
Data Science Research Center, Duke Kunshan University, Kunshan 215316, China

yangjl18@szu.edu.cn

yang.liu2@dukekunshan.edu.cn

wanjy@sjtu.edu.cn

Received Date: 7 July 2025
Revised Date: 6 September 2025
Accepted Date: 11 September 2025
Available Online: 12 September 2025

Figures(4)

The oxygen evolution reaction (OER) is crucial for renewable energy systems, such as water splitting and metal air batteries. However, the slow kinetics of OER significantly limits the overall energy conversion efficiency, necessitating effective catalysts. The multielement transition metal oxides offer a promising alternative compared to precious metal oxides, yet their composition optimization remains challenging due to the vastness of the combinatorial space. Traditional trial-and-error approaches are labor-intensive and inefficient. To address this challenge, we develop an innovative automated platform integrating machine learning (ML) with Bayesian optimization for rapid and cost-effective synthesis and evaluation of electrocatalysts. This platform allows the automation of the entire experimental process, from synthesis to evaluation, enabling real-time feedback and guiding subsequent experiments. In a continuous operation of 32 h, the platform conducted 96 experiments to optimize the composition of (Ni-Fe-Co-Mn-Mo)O_x, resulting in an electrocatalyst with an overpotential of 231 mV at 10 mA/cm². This automated approach significantly reduces manual intervention and enhances efficiency, proving to be a valuable tool for optimizing materials in complex, multidimensional spaces.
- Machine learning,
- Electrocatalysts,
- Automation,
- Oxygen evolution reaction,
- Transition metal oxides
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