Automated Adsorption Workflow for Semiconductor Surfaces and the Application to Zinc Telluride.

Area of Science:

• Materials Science
• Computational Chemistry
• Surface Science

Background:

• Surface adsorption is critical for heterogeneous catalysis efficiency.
• Predicting adsorption behavior is key to discovering new catalytic materials.

Purpose of the Study:

• To present an automated, high-throughput workflow for calculating semiconductor surface adsorption.
• To generate comprehensive adsorption descriptors for catalysis research.

Main Methods:

• Utilized density functional theory (DFT) calculations.
• Developed an automated workflow including surface search and adsorption structure construction.
• Computed energy, charge, geometric, and electronic descriptors.

Main Results:

• Generated high-throughput adsorption data for semiconductors.
• Successfully applied the workflow to zinc telluride for CO2 reduction photocatalysis.
• Created a database of computed descriptors.

Conclusions:

• The automated workflow accelerates the discovery of semiconductor materials for catalysis.
• This method provides a powerful tool for identifying efficient photocatalysts.
• The generated descriptors aid in understanding and predicting catalytic performance.