论文标题
在全尺寸的独立石墨烯上H原子的实验验证的神经网络势能表面
An experimentally validated neural-network potential energy surface for H atoms on free-standing graphene in full dimensionality
论文作者
论文摘要
我们提出了描述与独立石墨烯相互作用的氢原子的原子间力的第一原理质量势能表面（PES）。 PES是一种高维神经网络电位，已通过使用PBE-D2功能的密度功能理论计算为75945个数据点。该神经网络改善了先前发表的PES（Jiang等人，Science，2019，364，379），表现出了现实的物理吸附，并降低了RMS拟合误差的10倍，即0.6 MeV/原子。我们使用此PE来计算约150万个经典轨迹，并经过精心选择的初始条件，以直接与以1.9 eV的发病率转换能量进行的H-和D原子散射实验的结果进行比较。尽管有良好的均匀均匀，但理论上预测的散射角度和能量损失分布是良好的，尽管该实验是prageenne craplent praperents praperents praperents praperents praperents progence craplent preperents pr graperenne congrence graperents graperents grageence graperent graperenne。实验和理论之间的剩余差异可能是由于PT底物的影响，仅在实验中存在。
We present a first principles-quality potential energy surface (PES) describing the inter-atomic forces for hydrogen atoms interacting with free-standing graphene. The PES is a high-dimensional neural network potential that has been parameterized to 75945 data points computed with density-functional theory employing the PBE-D2 functional. Improving over a previously published PES (Jiang et al., Science, 2019, 364, 379), this neural network exhibits a realistic physisorption well and achieves a 10-fold reduction in the RMS fitting error, which is 0.6 meV/atom. We used this PES to calculate about 1.5 million classical trajectories with carefully selected initial conditions to allow for direct comparison to results of H- and D-atom scattering experiments performed at incidence translational energy of 1.9 eV and a surface temperature of 300 K. The theoretically predicted scattering angular and energy loss distributions are in good agreement with experiment, despite the fact that the experiments employed graphene grown on Pt(111). The remaining discrepancies between experiment and theory are likely due to the influence of the Pt substrate only present in the experiment.
