Constructing High-Dimensional Neural Network Potential Energy Surfaces for Gas–Surface Scattering and Reactions

Abstract

While the ab initio molecular dynamics (AIMD) approach to gas–surface interaction has been instrumental in exploring important issues such as energy transfer and reactivity, it is only amenable to short-time events and a limited number of trajectories because of the on-the-fly nature of the density functional theory (DFT) calculations. Here, we report a high-dimensional global reactive potential energy surface (PES) constructed with high fidelity from judiciously placed DFT points, using a machine learning method; and it is orders-of-magnitude more efficient than AIMD in dynamical calculations and can be employed in various simulations without performing additional electronic structure calculations. Importantly, the surface atoms are included in such a PES, which provides a unique platform for studying energy transfer and scattering/reaction of the impinging molecule on the solid surface on an equal footing.