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

Area of Science:

• Materials Science and Engineering
• Catalysis
• Computational Chemistry

Background:

• Identifying active sites is crucial for catalyst optimization, particularly in complex high-entropy materials.
• Predicting catalytic activity and overpotential in these materials presents significant challenges due to numerous random sites.

Purpose of the Study:

• To develop an advanced predictive model for accurately identifying active sites and their associated overpotentials.
• To screen a large number of high-entropy catalysts for optimal oxygen evolution reaction (OER) performance.
• To identify specific elemental compositions and coordination environments that enhance catalytic activity.

Main Methods:

• Developed an attention-enhanced, multiobjective predictive model.
• Applied the model to predict OER overpotentials and doping formation energies in high-entropy CoOOH materials.
• Screened 17,500 potential catalysts, followed by automated synthesis and experimental validation.

Main Results:

• Identified 8 catalysts with optimal catalytic activity from the screening.
• Discovered TiFeNiZn-CoOOH, exhibiting an exceptional OER overpotential of 263 mV at 100 mA/cm².
• Confirmed Zn's high active site occupation probability and the [CoNiZn] coordination's role in minimizing overpotential.

Conclusions:

• The developed predictive model precisely identifies active sites and overpotentials in high-entropy catalysts.
• Zn incorporation and specific coordination environments significantly enhance OER catalytic activity by activating gap states.
• This approach provides a powerful framework for discovering high-performance catalysts with predictable structures.