- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Notes
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Erlangen, Nürnberg, Univ., Diss., 2006
- Keyword
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Dichtefunktionalformalismus ; Festkörpertheorie ; Magnetismus ; Computerphysik
- Creator
- URN
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urn:nbn:de:bvb:29-opus-4132
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 10:46 AM CEST
Data provider
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Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Object type
- Hochschulschrift
Associated
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Resolution of identity approach for the Kohn-Sham correlation energy within the exact-exchange random-phase approximation
Spectral approximation scheme for a hybrid, spin-density Kohn–Sham density-functional theory in an external (nonuniform) magnetic field and a collinear exchange-correlation energy
Numerical Methods for Kohn-Sham Density Functional Theory (REVIEW)
Kohn-Sham SCE formulation for molecules and lattice problems
Optimal power series expansions of the Kohn–Sham potential
Multi-configuration Kohn-Sham methods with orbital-dependent functionals
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Anderson localization in the Kohn-Sham model for disordered crystals
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Efficient Exact-Exchange Kohn-Sham Methods including Magnetization-Currents and Finite Temperatures
Efficient exact-exchange Kohn-Sham methods including magnetization-currents and finite temperatures : = Effiziente Kohn-Sham Methoden mit exaktem Austausch einschließlich Magnetisierungsströme und endlicher Temperaturen
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Spectral approximation scheme for a hybrid, spin-density Kohn–Sham density-functional theory in an external (nonuniform) magnetic field and a collinear exchange-correlation energy
Numerical Methods for Kohn-Sham Density Functional Theory (REVIEW)
Kohn-Sham SCE formulation for molecules and lattice problems
Optimal power series expansions of the Kohn–Sham potential
Multi-configuration Kohn-Sham methods with orbital-dependent functionals
An exact chiral amorphous spin liquid
Anderson localization in the Kohn-Sham model for disordered crystals
Rapidly predicting Kohn–Sham total energy using data-centric AI