An experimentally validated neural-network potential energy surface for H-atom on free-standing graphene in full dimensionality-Reference-Cited by-同舟云学术

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

Published:2020 Issue:45 Volume:22 Page:26113-26120
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Wille Sebastian¹²³⁴⁵,Jiang Hongyan¹²³⁴^ORCID,Bünermann Oliver⁵⁶³⁴⁷^ORCID,Wodtke Alec M.¹²³⁴⁵^ORCID,Behler Jörg⁵⁸⁶³⁴^ORCID,Kandratsenka Alexander¹²³⁴^ORCID

Affiliation:

1. Department of Dynamics at Surfaces

2. Max-Planck-Institute for Biophysical Chemistry

3. 37077 Göttingen

4. Germany

5. Institute for Physical Chemistry

6. Georg-August University of Göttingen

7. International Center for Advanced Studies of Energy Conversion

8. Theoretische Chemie

Abstract

We present a first principles-quality neural-network potential energy surface describing interactions for a hydrogen atom with free-standing graphene.

Funder

H2020 European Research Council

Deutsche Forschungsgemeinschaft

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2020/CP/D0CP03462B

Reference44 articles.

1. Hydrogen-storage materials for mobile applications

2. Importance of Surface Morphology in Interstellar H 2 Formation