Quasi-Classical Trajectory with Adsorbate Gaussian Binning: Quantum-State-Resolved Prediction of Dissociative...

Area of Science:

• Surface Science
• Chemical Dynamics
• Computational Chemistry

Background:

• Dissociative chemisorption (DC) is crucial for interfacial applications.
• Predicting DC sticking probabilities (S0) for polyatomic molecules on metals is difficult.
• Existing methods like quantum dynamics are computationally expensive, and quasi-classical trajectory (QCT) methods suffer from energy leakage.

Purpose of the Study:

• To apply and validate a new QCT approach with adsorbate Gaussian binning (QCT-AGB).
• To model the dissociative chemisorption of methane on Ni(111) using QCT-AGB.
• To compare simulation results with experimental data across various conditions.

Main Methods:

• Utilized a novel QCT-AGB method.
• Employed a first-principles neural network potential for simulations.
• Simulated the dissociative chemisorption of methane (CH4) on a Ni(111) surface.

Main Results:

• QCT-AGB achieved excellent agreement with experimental data.
• Accurate prediction of sticking probabilities (S0) across collision energies and vibrational states.
• Correctly reproduced branching ratios for different isotopologue channels.

Conclusions:

• QCT-AGB is a validated, efficient, and reliable computational method.
• The approach enables accurate modeling of quantum-state-resolved dissociative chemisorption.
• This work advances the understanding of molecule-surface interactions in catalysis and materials science.