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

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 (S₀) 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.