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.

中文翻译：

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构造用于气体-表面散射和反应的高维神经网络势能面

虽然从头分子动力学（AIMD）方法用于气体-表面相互作用一直在探索重要问题（例如能量转移和反应性）方面发挥了作用，由于它的动态特性，它仅适用于短时间事件和有限的轨迹。密度泛函理论（DFT）计算。在这里，我们报告了使用机器学习方法从明智放置的DFT点以高保真度构造的高维全局无功势能面（PES）；在动态计算中，它比AIMD效率高出几个数量级，并且可以在各种模拟中使用，而无需执行额外的电子结构计算。重要的是，表面原子包含在此类PES中，