Decoding active sites in high-entropy catalysts via attention-enhanced model.

Abstract

Identifying active sites is decisive for optimizing catalysts, but this remains challenging, especially in high-entropy materials with multiple random sites. Here, we developed an attention-enhanced, multiobjective predictive model to precisely identify active sites and their corresponding overpotentials, a key parameter for catalytic activity. Using this model to predict the overpotential of oxygen evolution reaction (OER) process and doping formation energies in high-entropy CoOOH materials, we screened 17,500 catalysts and identified 8 with optimal catalytic activity. Subsequent automated synthesis and validation found a high-performance catalyst, TiFeNiZn-CoOOH, which exhibited an exceptional OER overpotential of 263 millivolts at 100 milliamperes per square centimeter. Feature importance and statistical analysis of more than 5 million structures confirmed that Zn consistently shows the highest active site occupation probability, and the [CoNiZn] coordination yields the lowest overpotential. Electronic structure analysis revealed that Zn activates gap states, critically lowering the OER energy barrier. This work paves a broad avenue for screening high-performance catalysts with identified catalyst structures.