Book/Dissertation / PhD Thesis

FZJ-2022-03102

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png High-Performance Computing Approach to Hybrid Functionals in the All-Electron DFT Code FLEUR

Redies, M. (Corresponding author)FZJ*

2022
Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag Jülich
ISBN: 978-3-95806-639-7

Please use a persistent id in citations: http://hdl.handle.net/2128/31792

Abstract: Virtual materials design attempts to use computational methods to discover new materials with superior properties within the vast space of all conceivable materials. Density-functional theory (DFT) is central to this field, enabling scientists to predict material properties from first principles, i.e. without relying on external parameters or experimental values. While standard DFT is capable of predicting many materials with satisfying accuracy, it struggles with some properties such as details of the electronic structure or certain material classes, e.g. materials exhibiting strongly correlated electrons. This has created a need for methods with greater predictive power. One such class of methods are hybrid exchange-correlation functionals which combine the exact Hartree-Fock exchange with local exchange-correlation functionals, resulting in highly accurate predictions for many insulating or semiconductor materials. However, the computational cost of hybrid functionals increases rapidly with system size and limits their application to small systems. This thesis aims to solve the computational challenge posed by hybrid functionals in large systems by utilizing the massive computational power of today’s supercomputers

Note: Dissertation, RWTH Aachen University, 2022

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Contributing Institute(s):

1. Quanten-Theorie der Materialien (PGI-1)
2. Quanten-Theorie der Materialien (IAS-1)
3. JARA-FIT (JARA-FIT)
4. JARA - HPC (JARA-HPC)

Research Program(s):

1. 5211 - Topological Matter (POF4-521) (POF4-521)

Appears in the scientific report 2022

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