Automated Generation of Microkinetics for Heterogeneously Catalyzed Reactions Considering Correlated Uncertainties

Area of Science:

• Computational Chemistry
• Chemical Kinetics
• Materials Science

Background:

• Microkinetic modeling is crucial for understanding heterogeneous catalysis.
• Quantifying uncertainties in first-principles calculations is essential for reliable predictions.
• Combustion engine exhaust gas oxidation over platinum catalysts is a key industrial process.

Purpose of the Study:

• To develop an automated framework for constructing microkinetic models with correlated uncertainty quantification.
• To investigate the oxidation reactions of exhaust gas emissions over Pt(111) using first-principles calculations.
• To identify a reliable microkinetic mechanism through multiscale modeling and experimental comparison.

Main Methods:

• Ab-initio based framework for automated microkinetic model construction.
• Generation of 2000 microkinetic models within the uncertainty space of the BEEF-vdW functional.
• Multiscale modeling to compare simulation results with experimental data for oxidation reactions.

Main Results:

• An ensemble of simulations highlighted the significance of considering uncertainties in energetic parameters.
• A microkinetic mechanism was identified that accurately reproduces experimental data for exhaust gas oxidation.
• The study suggests Pt(111) as the active site for light hydrocarbon oxidation.

Conclusions:

• The developed framework enables automated reaction mechanism construction with correlated uncertainty quantification.
• This approach facilitates DFT-constrained microkinetic model optimization for various catalytic systems.
• The findings contribute to a deeper understanding of catalytic oxidation processes and catalyst design.