Berlin 2018 – wissenschaftliches Programm
Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe
TT: Fachverband Tiefe Temperaturen
TT 46: Poster Session: Graphene (joint session O/TT)
TT 46.10: Poster
Dienstag, 13. März 2018, 18:15–20:30, Poster A
Development of a high-dimensional neural network potential for hydrogen atoms at graphene. — •Sebastian Wille1,2, Marvin Kammler2, Martín L. Paleico3, Jörg Behler3, Alec M. Wodtke1,2, and Alexander Kandratsenka2 — 1Institute for Physical Chemistry, Georg-August University Göttingen, Germany — 2Department of Dynamics at Surfaces, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany — 3Theoretical Chemistry, Georg-August University Göttingen, Germany
To fully understand atom-surface interactions, the availability of an accurate full-dimensional potential energy surface (PES) is crucial. High-dimensional neural network potentials have been shown to provide very accurate PESs for a wide range of systems. Here, we develop a neural network potential for H-atom scattering from a graphene sheet. We fit the potential to density functional theory energies calculated on-the-fly in ab initio molecular dynamics simulations. We find that the procedure can reliably describe H at graphene, which makes it possible to effectively simulate the scattering for this system in a large range of incidence conditions.