An experimentally validated neural-network potential energy surface for H atoms on free-standing graphene in full dimensionality

TL;DR

This paper develops a highly accurate neural network potential energy surface for hydrogen atoms on free-standing graphene, validated through extensive classical trajectory simulations that align well with experimental scattering data.

Contribution

It introduces a new neural network PES with significantly reduced error, improving modeling of H-graphene interactions over previous models.

Findings

Neural network PES achieves 0.6 meV/atom RMS error.

Simulated scattering distributions match experimental results.

Discrepancies likely due to substrate effects in experiments.

Abstract

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.