Functional designed to include surface effects in self-consistent density functional theory

By Rickard Armiento, Ann E. Mattsson. 2005

452 citations recorded by the source corpus.

We design a density-functional-theory DFT exchange-correlation functional that enables an accurate treatment of systems with electronic surfaces. Surface-specific approximations for both exchange and correlation energies are developed. A subsystem functional approach is then used: an interpolation index combines the surface functional with a functional for interior regions. When the local density approximation is used in the interior, the result is a straightforward functional for use in self-consistent DFT. The functional is validated for two metals Al, Pt and one semiconductor Si by calculations of i established bulk properties lattice constants and bulk moduli and ii a property where surface effects exist the vacancy formation energy. Good and coherent results indicate that this functional may serve well as a universal first choice for solid-state systems and that yet improved functionals can be constructed by this approach.

Subfields: Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/3ded736a3bc68535c348aba1ef8c479df1e3c6d3

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In Atomic and Molecular Physics, and Optics

Citations in the corpus

exciting: a full-potential all-electron package implementing density-functional theory and many-body perturbation theory (2014, background): “…LDA von Barth–Hedin [14] LSDA Perdew–Burke–Ernzerhof (PBE) [64] GGA Revised PBE [102] GGA PBE for solids (PBEsol) [66] GGA Wu–Cohen exchange (WC06) with GGA PBE correlation [100] Armiento–Mattsson (AM05) [12] GGA Finally, the last term of equation (57) is where (cid:2) xc is the xc energy density.”
Libxc: A library of exchange and correlation functionals for density functional theory (2012, background): “…exchange (for solids) 19.33 [75] XC GGA X RPBE Hammer, Hansen & Norskov (PBE-like) 17.14 [76] XC GGA X WC Wu & Cohen 17.91 [77] 10 XC GGA X AM05 Armiento & Mattsson 05 exchange 61.38 [78, 79] XC GGA X PBEA Madsen 07 30.98 [80] XC GGA X mPW91 mPW91 of Adamo & Barone 47.19 [81] XC GGA X 2D B86…”
Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach. (2015, methodology): “We note at this point that in ∆-ML models other baseline methods, such as extended H¨uckel, tight-binding DFT [42], OM2 [37], or AM05 [43] could have been used just as well.”