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Toward Practical Design of High-Entropy Catalysts for Chlorine Evolution Reaction via Pareto-Guided Multi-Objective
Ruyu Yang1, Donglai Zhou1, Zijin Jia1
1State 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.
Angewandte Chemie (International Ed. in English)
|May 13, 2026
Summary
High-entropy ruthenium oxides (Ru-HEO) offer improved chlorine evolution reaction (CER) performance over traditional catalysts. An AI-driven optimization framework identified novel Ru-HEO compositions balancing activity, selectivity, and cost for sustainable chemical production.
Area of Science:
- Materials Science
- Electrochemistry
- Catalysis
Background:
- The chlorine evolution reaction (CER) is crucial for the chlor-alkali industry.
- Conventional ruthenium dioxide (RuO2) catalysts exhibit parasitic oxygen evolution, reducing efficiency.
- High-entropy ruthenium oxides (Ru-HEO) show promise but require understanding complex composition-structure-performance relationships.
Purpose of the Study:
- To develop a Pareto-guided multi-objective Bayesian optimization framework for autonomous exploration of quinary Ru-HEO systems.
- To identify Ru-HEO compositions that balance catalytic activity, chlorine selectivity, and material cost.
- To establish an AI-accelerated research paradigm for designing high-performance electrocatalysts.
Main Methods:
- Constructed a Pareto-guided multi-objective Bayesian optimization framework.
- Employed autonomous high-throughput exploration of quinary Ru-HEO systems.
- Integrated multi-objective optimization with robotic experimentation.
Main Results:
- Identified Ru-HEO compositions balancing mass activity, Cl2 selectivity, and cost.
- The leading Ru-HEO catalyst (8.4 at% Ru) achieved 5083 A g−1 Ru activity at 1.50 V vs RHE with 100-h stability.
- Achieved >95% selectivity and 14.6% solar-to-chemical (STC) efficiency in a PV-EC device, with projected costs of $0.177/kg Cl2.
Conclusions:
- The AI-driven framework enables efficient discovery of high-performance electrocatalysts.
- The developed Ru-HEO catalyst outperforms commercial RuO2 and state-of-the-art alternatives.
- This approach offers a generalizable pathway for sustainable chemicals manufacturing.
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