Jump to main content
Jump to site search
PLANNED MAINTENANCE Close the message box

Scheduled maintenance work on Wednesday 21st October 2020 from 07:00 AM to 07:00 PM (BST).

During this time our website performance may be temporarily affected. We apologise for any inconvenience this might cause and thank you for your patience.



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

Author affiliations

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

Back to tab navigation

Supplementary files

Article information


Submitted
28 Jun 2020
Accepted
04 Sep 2020
First published
04 Sep 2020

This article is Open Access

Phys. Chem. Chem. Phys., 2020, Advance Article
Article type
Paper

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, Advance Article , DOI: 10.1039/D0CP03462B

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. Material from this article can be used in other publications provided that the correct acknowledgement is given with the reproduced material.

Reproduced material should be attributed as follows:

  • For reproduction of material from NJC:
    [Original citation] - Published by The Royal Society of Chemistry (RSC) on behalf of the Centre National de la Recherche Scientifique (CNRS) and the RSC.
  • For reproduction of material from PCCP:
    [Original citation] - Published by the PCCP Owner Societies.
  • For reproduction of material from PPS:
    [Original citation] - Published by The Royal Society of Chemistry (RSC) on behalf of the European Society for Photobiology, the European Photochemistry Association, and RSC.
  • For reproduction of material from all other RSC journals:
    [Original citation] - Published by The Royal Society of Chemistry.

Information about reproducing material from RSC articles with different licences is available on our Permission Requests page.


Social activity

Search articles by author

Spotlight

Advertisements