Issue 45, 2020

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

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 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.

Graphical abstract: An experimentally validated neural-network potential energy surface for H-atom on free-standing graphene in full dimensionality

Supplementary files

Article information

Article type
Paper
Submitted
28 jun. 2020
Accepted
04 sep. 2020
First published
04 sep. 2020
This article is Open Access
Creative Commons BY license

Phys. Chem. Chem. Phys., 2020,22, 26113-26120

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

S. Wille, H. Jiang, O. Bünermann, A. M. Wodtke, J. Behler and A. Kandratsenka, Phys. Chem. Chem. Phys., 2020, 22, 26113 DOI: 10.1039/D0CP03462B

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