Entropy-guided discovery of denary trirutile antimonates for electrocatalytic chlorine evolution

Developing non-noble metal-based chlorine evolution reaction (CER) catalysts to compete with noble metals-containing dimensionally stable anodes is challenging. Multi-metal oxides are promising for CER, but their discovery heavily depends on human-driven experimentation. Herein, an atomic-level entropy-guided strategy combining density functional theory (DFT) and data-driven machine learning (ML) was developed to accelerate the discovery of non-noble metal-based MSb₂O₆-type trirutile antimonates for CER. The high-entropy effect could benefit CER with excellent activity and stability by optimizing the electronic structure. High-entropy trirutile antimonates, with oxygen vacancies and lattice strain, reduce the energy barrier at Cu sites for Cl∗ adsorption, achieving a record-low overpotential of 24 mV at 10 mA cm⁻², >95% faradaic efficiency, and 160-h stability at 50 mA cm⁻². The presented atomic-level entropy-guided strategy would inspire the rational design of highly active and stable electrocatalysts for CER and other electrocatalysis applications.