Best-of-Both-Worlds Predictive Approach to Dissociative Chemisorption on Metals.

Area of Science:

• Computational chemistry
• Surface science
• Heterogeneous catalysis

Background:

• Dissociative chemisorption on metal surfaces is vital for heterogeneous catalysis.
• Accurate and affordable theoretical methods are needed to describe these reactions.
• Existing methods often lack the required predictive capability or are computationally expensive.

Purpose of the Study:

• To develop a predictive computational approach for dissociative chemisorption.
• To achieve near Diffusion Monte Carlo (DMC) accuracy at the cost of Density Functional Theory (DFT).
• To accurately model the H2 + Al(110) system, including energy dissipation and quantum tunneling.

Main Methods:

• Utilizing Diffusion Monte Carlo (DMC) to determine minimum barrier heights.
• Constructing a density functional that accurately reproduces these barrier heights.
• Performing dynamics calculations to scrutinize energy dissipation and quantum tunneling effects.

Main Results:

• A novel predictive approach for potential energy surface construction was developed.
• The method achieves near DMC accuracy while maintaining DFT computational costs.
• Calculations reproduced molecular beam sticking experiments for H2 + Al(110) with an accuracy of ~1.4 kcal/mol.

Conclusions:

• The developed approach provides a highly accurate and affordable method for describing dissociative chemisorption.
• This method holds significant promise for advancing the understanding and design of heterogeneous catalysts.
• The findings demonstrate the capability of the approach to capture complex reaction dynamics, including quantum effects.