EDMFTF

EDMFTF (Embedded Dynamical Mean Field Theory Functional) is a DFT+DMFT implementation developed by Kristjan Haule at Rutgers University. It provides a sophisticated interface for performing charge self-consistent DFT+DMFT calculations wi…

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Overview

EDMFTF (Embedded Dynamical Mean Field Theory Functional) is a DFT+DMFT implementation developed by Kristjan Haule at Rutgers University. It provides a sophisticated interface for performing charge self-consistent DFT+DMFT calculations with advanced impurity solvers, focusing on strongly correlated materials with realistic crystal structures.

Reference Papers (1)

Full Documentation

Official Resources

  • Homepage: https://hauleweb.rutgers.edu/EDMFTF/
  • Documentation: https://hauleweb.rutgers.edu/EDMFTF/index.html
  • Source Repository: Available via homepage registration
  • License: Free for academic use (registration required)

Overview

EDMFTF (Embedded Dynamical Mean Field Theory Functional) is a DFT+DMFT implementation developed by Kristjan Haule at Rutgers University. It provides a sophisticated interface for performing charge self-consistent DFT+DMFT calculations with advanced impurity solvers, focusing on strongly correlated materials with realistic crystal structures.

Scientific domain: Strongly correlated electron systems, transition metal oxides, heavy fermions, actinides
Target user community: Researchers studying strongly correlated materials requiring DFT+DMFT methodology

Theoretical Methods

  • Density Functional Theory + Dynamical Mean Field Theory (DFT+DMFT)
  • Charge self-consistent DFT+DMFT
  • Continuous-time quantum Monte Carlo (CTQMC) impurity solver
  • Hybridization expansion (CT-HYB)
  • Exact diagonalization (ED) impurity solver
  • Non-crossing approximation (NCA)
  • One-crossing approximation (OCA)
  • Hubbard I approximation
  • Full Coulomb vertex implementation
  • Spin-orbit coupling
  • LDA+DMFT, GGA+DMFT
  • Non-collinear magnetism

Capabilities (CRITICAL)

  • Charge self-consistent DFT+DMFT calculations
  • Electronic structure of strongly correlated materials
  • Spectral functions and DOS including correlation effects
  • Magnetic properties (moments, ordering)
  • Metal-insulator transitions
  • Orbital ordering and occupations
  • Crystal field effects in correlated systems
  • Temperature-dependent properties
  • Pressure-dependent studies
  • Heavy fermion systems
  • Actinide materials (5f electrons)
  • Transition metal oxides (3d electrons)
  • Rare earth systems (4f electrons)
  • Momentum-resolved spectral functions
  • Optical conductivity
  • Thermodynamics of correlated systems

Sources: Official EDMFTF website (https://hauleweb.rutgers.edu/EDMFTF/), cited in 6/7 source lists

Key Features

Charge Self-Consistency:

  • Full charge self-consistency between DFT and DMFT
  • Iterative solution of DFT and DMFT equations
  • Convergence acceleration techniques
  • Proper treatment of charge redistribution

Advanced Impurity Solvers:

  • Multiple solver options for different regimes
  • CTQMC for general multi-orbital problems
  • ED for small clusters or strong coupling
  • NCA/OCA for heavy fermion physics
  • Solver selection based on physics

Realistic Materials:

  • Interfaces with LAPW codes (WIEN2k)
  • Full crystal structure handling
  • Arbitrary number of correlated orbitals
  • Multiple correlated atoms per unit cell
  • Spin-orbit coupling included

Sophisticated Physics:

  • Full Coulomb vertex (beyond density-density)
  • Crystal field splitting
  • Ligand field effects
  • Covalency and hybridization
  • Multi-orbital correlations

Inputs & Outputs

  • Input formats:

    • DMFT input file (PARAMS)
    • DFT output from WIEN2k (case.struct, etc.)
    • Wannier projections or projectors
    • Coulomb interaction parameters (U, J)
    • CTQMC parameters (beta, number of steps)
  • Output data types:

    • Self-energy (Matsubara frequencies)
    • Green's functions (local and momentum-resolved)
    • Spectral functions and DOS
    • Occupation matrices
    • Magnetic moments
    • Total energy
    • Convergence data
    • Observable files for analysis

Interfaces & Ecosystem

  • DFT code integration:

    • Primary interface: WIEN2k (LAPW method)
    • Tight integration with WIEN2k workflow
    • Reads WIEN2k outputs directly
  • Impurity solver interfaces:

    • CTQMC solver included
    • Interface to exact diagonalization
    • NCA/OCA solvers available
  • Wannier functions:

    • Internal projector generation
    • Can use pre-computed Wannier functions
    • Flexible orbital selection
  • Analysis tools:

    • Python scripts for post-processing
    • Spectral function analysis
    • Analytical continuation tools

Workflow and Usage

Typical DFT+DMFT Workflow:

  1. DFT Calculation:

    • Run standard WIEN2k DFT calculation
    • Converge electronic structure
    • Generate necessary files
  2. Setup DMFT:

    • Define correlated orbitals
    • Set interaction parameters (U, J)
    • Choose impurity solver
    • Set temperature and CTQMC parameters
  3. DMFT Iterations:

    • Run DMFT self-consistency loop
    • Solve impurity problem at each iteration
    • Update DFT charge density
    • Monitor convergence
  4. Analysis:

    • Extract spectral functions
    • Compute physical observables
    • Perform analytical continuation
    • Compare with experiments

Parameter Selection:

  • U and J: From constrained RPA or literature
  • Temperature: Physical temperature or convergence parameter
  • Double counting: Various schemes (FLL, AMF, etc.)
  • Projectors: Atomic-like or Wannier-like

Advanced Capabilities

Metal-Insulator Transitions:

  • Pressure or doping-induced transitions
  • Temperature-driven transitions (Mott physics)
  • Orbital-selective Mott transitions
  • Volume collapse transitions

Magnetic Properties:

  • Paramagnetic, ferromagnetic, antiferromagnetic states
  • Magnetic phase diagrams
  • Spin and orbital moments
  • Magnetic exchange interactions

Spectroscopy:

  • Photoemission spectroscopy (PES/ARPES) simulation
  • X-ray absorption spectroscopy (XAS)
  • Optical conductivity
  • Momentum-resolved spectra

Thermodynamics:

  • Entropy calculations
  • Specific heat
  • Free energy
  • Phase stability

Computational Efficiency

  • CTQMC solver: Most expensive component
  • Typical iteration: Hours to days depending on system
  • Convergence: 10-50 DMFT iterations typically
  • Parallelization: CTQMC parallelized via MPI
  • Total calculation: Days to weeks for production runs

Limitations & Known Constraints

  • Computational cost: Very expensive; CTQMC-DMFT intensive
  • WIEN2k dependency: Requires WIEN2k for DFT part
  • Registration required: Free but needs registration
  • Learning curve: Very steep; requires deep understanding of DMFT
  • Statistical noise: CTQMC introduces Monte Carlo errors
  • Analytical continuation: MaxEnt adds systematic uncertainty
  • Parameter dependence: Results depend on U, J, double counting
  • System size: Limited to relatively small unit cells
  • Temperature: Low temperatures computationally demanding
  • Documentation: Good but assumes DMFT expertise
  • Platform: Linux/Unix; requires proper build environment

Application Areas

Transition Metal Oxides:

  • Cuprates (high-Tc superconductors)
  • Manganites (colossal magnetoresistance)
  • Vanadates, titanates
  • Ruthenates

Heavy Fermion Systems:

  • Cerium and Ytterbium compounds
  • Kondo lattice physics
  • Valence fluctuations
  • Quantum criticality

Actinides:

  • Plutonium and other 5f systems
  • Volume collapse transitions
  • Magnetic properties
  • Electronic structure

Iron-Based Superconductors:

  • Iron pnictides and chalcogenides
  • Orbital selectivity
  • Magnetic order

Comparison with Other DMFT Codes

  • vs TRIQS: EDMFTF more focused on charge self-consistency
  • vs w2dynamics: EDMFTF integrates with WIEN2k (LAPW)
  • vs DMFTwDFT: Similar functionality, different implementations
  • Unique strength: Sophisticated charge self-consistency, Haule's expertise

Best Practices

Convergence:

  • Start with Hubbard I or non-self-consistent
  • Gradually increase CTQMC accuracy
  • Monitor charge self-consistency carefully
  • Use charge mixing for stability

Parameter Choice:

  • Validate U, J from constrained calculations
  • Test double counting scheme sensitivity
  • Consider multiple temperature points
  • Check projector dependence

Verification:

  • Compare with experiments (PES, XAS, etc.)
  • Check sum rules and conservation laws
  • Validate against known limits
  • Perform convergence studies

Verification & Sources

Primary sources:

  1. Official website: https://hauleweb.rutgers.edu/EDMFTF/
  2. Documentation: https://hauleweb.rutgers.edu/EDMFTF/index.html
  3. K. Haule et al., Phys. Rev. B 81, 195107 (2010) - Charge self-consistent DFT+DMFT
  4. K. Haule, Phys. Rev. B 75, 155113 (2007) - Quantum Monte Carlo impurity solver
  5. K. Haule and G. Kotliar, New J. Phys. 11, 025021 (2009) - Coherence-incoherence crossover
  6. P. Werner and A. J. Millis, Phys. Rev. B 74, 155107 (2006) - CT-HYB algorithm

Secondary sources:

  1. EDMFTF tutorials and workshops
  2. Published DFT+DMFT studies from Haule group
  3. Rutgers strongly correlated materials research
  4. Confirmed in 6/7 source lists (claude, g, gr, k, m, q)

Confidence: CONFIRMED - Appears in 6 of 7 independent source lists

Verification status: ✅ VERIFIED

  • Official homepage: ACCESSIBLE
  • Documentation: ACCESSIBLE
  • Software: Free for academics (registration required)
  • Community support: Active (Haule group, email support)
  • Academic citations: >300 (method and application papers)
  • Active use: Standard for charge self-consistent DFT+DMFT
  • Benchmark validation: Extensive comparisons with experiments
  • Developer: K. Haule (leading DMFT expert at Rutgers)

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