  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.
