Mode-specific and bond-selective dissociative chemisorption of CHD_3 and CH_2D_2 on Ni(111) revisited using a new potential energy surface

Dissociative chemisorption of methane on a nickel surface is a prototypical system for studying mode-specific chemistry in gassurface reactions.We recently developed a fifteen-dimensional potential energy surface for this system which has proven to be chemically accurate in reproducing the measured absolute dissociative sticking probabilities of CHD_3in thermal conditions and with vibrational excitation on Ni(111)at high incident energies.Here,using this new potential energy surface,we explored mode specificity and bond selectivity for CHD_3and CH_2D_2dissociative chemisorption at low incidence energies down to^50 k J/mol via a quasi-classical trajectory method.Our calculated dissociation probabilities are consistent with previous theoretical and experimental ones with an average shift in translational energy of^8 k J/mol.Our results very well reproduce the C–H/C–D branching ratio upon the C–H local mode excitation,which can be rationalized by the sudden vector projection model.Quantitatively,however,the calculated dissociative sticking probabilities are systematically larger than experimental ones,due presumably to the artificial zero point energy leakage into reaction coordinate.Further high-dimensional quantum dynamics calculations are necessary for acquiring a chemically accurate description of methane dissociative chemisorption at low incident energies.

Neural Network Representations for Studying Gas-Surface Reaction Dynamics:Beyond the Born-Oppenheimer Static Surface Approximation

In the past a few years,there has been significant progress in theoretical characterizations of gas-surface reaction dynamics at the atomic level.One of the major breakthroughs is the machine learning representations of the potential energy surfaces and related properties for molecules on metal surfaces from first-principles,particularly neural networks based methods.In this review,we focus on recent advances of the development and applications of high-dimensional symmetry-preserving neural network representations in gas-surface systems,which have enabled efficient Born-Oppenheimer molecular dynamics simulations with inclusion of all molecular and surface degrees of freedom,as well as some nonadiabatic molecular dynamics simulations with effective treatment of hot electrons,at the density function theory level.Despite these advances,further challenges remain.More accurate electronic structure theories and more efficient machine learning(and active learning)algorithms are needed towards a more quantitative description of more complex gas-surface reactions involving multiple surfaces and adsorbates or multiple electronic states.