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May 15, 2026Angewandte Chemie International Edition3 citations

Toward Practical Design of High‐Entropy Catalysts for Chlorine Evolution Reaction via Pareto‐Guided Multi‐Objective Bayesian Optimization Enabled by a Robotic AI‐Chemist

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RYRuyu YangDZDonglai ZhouZJZijin Jia

Key Points

  • The aim is to design high-entropy catalysts for the chlorine evolution reaction using advanced optimization methods.
  • Integrated AI-accelerated research with robotic experimentation
  • Utilized multi-objective bayesian optimization for catalyst design
  • Closed-loop approach to optimize production costs
  • Achieved production costs as low as $0.177 per kg
  • Established expedited pathways for electrocatalyst development
  • Demonstrated effectiveness of the closed-loop AI research paradigm

Abstract

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.

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Cite This Study

Yang et al. (2026) studied this question.

synapsesocial.com/papers/6a06b95be7dec685947abee5https://doi.org/10.1002/anie.8794274
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