Toward Practical Design of High‐Entropy Catalysts for Chlorine Evolution Reaction via Pareto‐Guided Multi‐Objective Bayesian Optimization Enabled by a Robotic AI‐Chemist
Abstract
ABSTRACT The electrocatalytic chlorine evolution reaction (CER) is essential to modern chlor‐alkali industry, yet conventional RuO 2 catalysts suffer from parasitic oxygen evolution. High‐entropy ruthenium oxides (Ru‐HEO) are promising alternatives, but their practical design is hindered by complex composition‐structure‐performance relationship. Herein, we construct a Pareto‐guided multi‐objective Bayesian optimization framework to enable autonomous high‐throughput exploration of quinary Ru‐HEO system. Through this trade‐off strategy, we identify compositions that efficiently balance mass activity, Cl 2 selectivity and material cost. The leading Ru‐HEO catalyst with only 8.4 at% Ru achieves a remarkable activity of 5083 A g −1 Ru at 1.50 V versus RHE and maintains excellent 100‐h stability, outperforming commercial RuO 2 and the state‐of‐the‐art catalysts reported. Integrated into a photovoltaic‐electrochemical (PV‐EC) prototype device and tested under simulated diurnal illumination, it sustains >95% selectivity, a maximum solar‐to‐chemical (STC) efficiency of 14.6% and projected Cl 2 production costs as low as $0.177 per kg. Our work establishes a closed‐loop, AI‐accelerated research paradigm that integrates multi‐objective optimization with robotic experimentation, offering a generalizable and expedited pathway toward high‐performance electrocatalysts for sustainable chemicals manufacturing.
Article Details
Authors (15)
Ruyu Yang
Donglai Zhou
State Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science
Zijin Jia
State Key Laboratory of Precision and Intelligent Chemistry Hefei National Research Center for Physical Sciences at the Microscale University of Science and Technology of China Hefei China
Yulan Han
Department of Chemistry and Biochemistry
Lianyou Tang
State Key Laboratory of Precision and Intelligent Chemistry Hefei National Research Center for Physical Sciences at the Microscale University of Science and Technology of China Hefei China
Zifan Jiang
State Key Laboratory of Precision and Intelligent Chemistry Hefei National Research Center for Physical Sciences at the Microscale University of Science and Technology of China Hefei China
Xiaolin Tai
State Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science
Yuhai Cai
State Key Laboratory of Precision and Intelligent Chemistry Hefei National Research Center for Physical Sciences at the Microscale University of Science and Technology of China Hefei China
Wenhui Zhong
Institute of Intelligent Innovation, Henan Academy of Sciences
Yue Lin
Hao Wang
Division of Quantitative Sciences, Department of Oncology Johns Hopkins University School of Medicine Baltimore Maryland USA
Jixian Xu
State Key Laboratory of Precision and Intelligent Chemistry Hefei National Research Center for Physical Sciences at the Microscale University of Science and Technology of China Hefei China
Yan Huang
Jun Jiang
State Key Laboratory of Precision and Intelligent Chemistry, Hefei National Research Center for Physical Sciences at the Microscale, School of Chemistry and Materials Science
Qing Zhu