2014/11/30 by Toma Susi, D. J. Mowbray, Duncan J. Mowbray +2 · 44 citations
Chemistry · Materials Science · Mathematics · Physics and Astronomy · #Algorithm #Atomic physics #Binding energy #Chemistry #Computational chemistry #Computer science #Density functional theory #Electron and X-Ray Spectroscopy Techniques #Electronic structure #Energy (signal processing) #Graphene #Graphene research and applications #Machine learning #Materials science #Mathematics #Nanotechnology #Nuclear magnetic resonance #Physics #Quantum mechanics #Surface and Thin Film Phenomena #X-ray photoelectron spectroscopy #cond-mat.mes-hall #cond-mat.mtrl-sci
paper · pdf · doi:10.1103/physrevb.91.081401
published in Physical Review B 91(8) (American Physical Society) · 5 pages, 3 figures, 2 tables
arxiv created 2014/12/11 · openalex publication_date 2015/02/02 · arxiv updated 2015/03/04 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
X-ray photoelectron spectroscopy combined with first-principles modeling is a powerful tool for determining the chemical composition and electronic structure of novel materials. Of these, graphene is an especially important model system for understanding the properties of other carbon nanomaterials. Here, we calculate the carbon 1s core level binding energy of pristine graphene using two methods based on density functional theory total energy differences: a calculation with an explicit core-hole, and an all-electron extension of the delta self-consistent field (\ensuremathΔSCF) method. We study systematically their convergence and computational workload, and the dependence of the energies on the chosen exchange-correlation functional. The \ensuremathΔSCF method is computationally more expensive, but gives consistently higher C 1s energies. Although there is a significant functional dependence, the binding energy calculated using the PBE functional is found to be remarkably close to what has been measured for graphite.