First Principles Identification of Active Sites in Heterogeneous Catalysis: A Case Study on Zn_xCr_yO_z for Syngas...

Area of Science:

• Catalysis
• Materials Science
• Computational Chemistry

Background:

• Discovering advanced heterogeneous catalysts requires understanding active site formation under realistic conditions.
• Computational methods are crucial for simulating complex catalytic processes and identifying structure-activity relationships.

Purpose of the Study:

• To develop a machine learning potential (MLP)-aided computational framework for simulating catalyst active site formation.
• To investigate syngas conversion over ZnₓCr_yO_z catalysts and identify active sites.
• To establish a generalized model for designing high-activity oxide/oxide catalysts.

Main Methods:

• Utilized a machine learning potential (MLP)-aided computational framework.
• Simulated catalyst preparation and reaction conditions.
• Employed CH-O bond dissociation as a descriptor for activity.
• Performed microkinetic analyses.

Main Results:

• Identified preferential segregation into ZnO and ZnCr₂O₄ phases, with ZnO forming a monolayer on ZnCr₂O₄.
• Determined the ZnO/ZnCr₂O₄(100) surface as the active surface under reaction conditions.
• Pinpointed geometrically linked oxygen vacancy pairs as the true active sites.
• Achieved kinetic results aligning with experimental observations.

Conclusions:

• The developed framework effectively tracks active site formation and deciphers structure-activity relationships.
• Geometrically linked oxygen vacancy pairs are identified as key active sites for syngas conversion.
• A generalized model for designing oxide/oxide catalysts is proposed, accelerating catalyst discovery.