Ab-initio Gutzwiller method: first application to Plutonium

Abstract

Except for small molecules, it is impossible to solve many electrons systems without imposing severe approximations. If the configuration interaction approaches (CI) or Coupled Clusters techniques \cite{FuldeBook} are applicable for molecules, their generalization for solids is difficult. For materials with a kinetic energy greater than the Coulomb interaction, calculations based on the density functional theory (DFT), associated with the local density approximation (LDA) \cite{Hohenberg64, Kohn65} give satisfying qualitative and quantitative results to describe ground state properties. These solids have weakly correlated electrons presenting extended states, like $sp$ materials or covalent solids. The application of this approximation to systems where the wave functions are more localized ($d$ or $f$-states) as transition metals oxides, heavy fermions, rare earths or actinides is more questionable and can even lead to unphysical results : for example, insulating FeO and CoO are predicted to be metalic by the DFT-LDA...

References in corpus (5)

• Multi-band Gutzwiller wave functions for general on-site interactions
• Ground State Theory of delta-Pu
• Experimental electronic heat capacities of $\alpha -$ and $\delta -$Plutonium; heavy-fermion physics in an element
• Slave-Boson Functional-Integral Approach to the Hubbard Model with Orbital Degeneracy
• Gutzwiller-correlated wave functions for degenerate bands: exact results in infinite dimensions

Cited by in corpus (8)

• LDA+Gutzwiller Method for Correlated Electron Systems: Formalism and Its Applications
• Gutzwiller density functional theory for correlated electron systems
• LDA+Gutzwiller Method for Correlated Electron Systems
• Electronic Correlation Induced Expansion of Compensated Electron and Hole Fermi Pockets in delta-Plutonium
• Numerical minimisation of Gutzwiller energy functionals
• The time-dependent Gutzwiller theory for multi-band Hubbard models
• Phase transitions of correlated systems from graph neural networks with quantum embedding techniques
• Strong parameter renormalization from optimum lattice model orbitals