An experimentally validated neural-network potential energy surface for H-atom on free-standing graphene in full dimensionality†
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 75 945 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 per atom. The chemisorption barrier is 172 meV, which is lower than that of the REBO-EMFT PES (260 meV). 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). Compared to previous calculations, the agreement with experiments is improved. The remaining discrepancies between experiment and theory are likely due to the influence of the Pt substrate only present in the experiment.
- This article is part of the themed collections: Emerging AI Approaches in Physical Chemistry and 2020 PCCP HOT Articles