Quasi-Classical Trajectory with Adsorbate Gaussian Binning: Quantum-State-Resolved Prediction of Dissociative Sticking Probability Made Simple

Dissociative chemisorption (DC) of molecules on solid surfaces is of both fundamental and practical importance in many interfacial applications. However, accurately predicting dissociative sticking probabilities (<i>S</i>₀) of polyatomic molecules on metal surfaces remains challenging. Fully coupled quantum dynamical methods are demanding, and conventional quasi-classical trajectory (QCT) methods are plagued by the zero-point energy leakage issue. Herein, we apply a newly developed QCT approach with adsorbate Gaussian binning (QCT-AGB) to the DC of methane on Ni(111), a key benchmark system. Utilizing a first-principles neural network potential, the QCT-AGB simulations achieve unprecedented agreement with experimental data across a wide range of collision energies and initial vibrational states, including branching ratios of different channels in isotopologues. This work validates QCT-AGB as an efficient and reliable approach for modeling quantum-state-resolved DC of polyatomic molecules on surfaces.