Predicting adsorption on metals: simple yet effective descriptors for surface catalysis.

Area of Science:

• Materials Science
• Computational Chemistry
• Surface Science

Background:

• Predicting molecular adsorption on metal surfaces is crucial for catalysis and materials design.
• Existing methods often require significant computational resources.

Purpose of the Study:

• To develop a simple, efficient, and computationally inexpensive model for predicting molecular adsorption on metal surfaces.
• To validate the model using both Density Functional Theory (DFT) calculations and experimental adsorption data.

Main Methods:

• A heuristic model was employed, utilizing six descriptors for metals and three for adsorptives.
• The model was trained and validated using DFT-calculated adsorption energies from literature.
• Experimental adsorption data for specific gas molecules on supported metal catalysts were also collected and modeled.

Main Results:

• The model accurately predicted chemisorption for a wide range of molecules on various metals with high predictive accuracy (Q(2) = 0.91-0.95).
• The model also performed well on experimental data, achieving R(2) = 0.95 and Q(2) = 0.86.
• Key descriptors included electronic properties, surface energy, and atomic/molecular size and mass.

Conclusions:

• A simple heuristic model can effectively predict molecular adsorption on metal surfaces.
• This approach offers a computationally efficient alternative for screening adsorption behaviors.
• The model's success with both DFT and experimental data highlights its broad applicability.