Official Resources
- Homepage: https://github.com/ComDMFT/ComDMFT
- Documentation: https://github.com/ComDMFT/ComDMFT/wiki
- Source Repository: https://github.com/ComDMFT/ComDMFT
- License: GNU General Public License v3.0
Overview
ComDMFT (Combination of DMFT codes) is an integrated DFT+DMFT package that combines multiple DFT codes with sophisticated DMFT impurity solvers. Developed by the Comscope group, it provides a unified framework for charge self-consistent DFT+DMFT calculations with emphasis on transition metal systems and strongly correlated materials.
Scientific domain: Strongly correlated materials, DFT+DMFT methodology, transition metal compounds
Target user community: Researchers performing DFT+DMFT calculations on correlated electron systems
Theoretical Methods
- DFT+DMFT (charge self-consistent)
- LDA+DMFT, GGA+DMFT
- Continuous-time quantum Monte Carlo (CTQMC)
- CT-HYB (hybridization expansion)
- Exact diagonalization (ED)
- Hubbard I approximation
- Spin-orbit coupling treatment
- Non-collinear magnetism
- GW+DMFT extensions
- Dual fermion approaches
- Cluster DMFT extensions
Capabilities (CRITICAL)
- Charge self-consistent DFT+DMFT
- Multiple DFT code backends (VASP, Wannier90, etc.)
- Multi-orbital strongly correlated systems
- Spectral functions with many-body effects
- Magnetic ordering calculations
- Metal-insulator transitions
- Orbital-selective correlations
- Temperature-dependent properties
- Momentum-resolved spectroscopy
- Optical properties with correlations
- Crystal field effects
- Covalency and ligand field
- Integration with ComCTQMC solver
- Flexible impurity solver selection
- Automated workflow management
- Python-based framework
Sources: Official ComDMFT repository (https://github.com/ComDMFT/ComDMFT), cited in 6/7 source lists
Key Features
Modular Architecture:
- Separation of DFT and DMFT components
- Multiple DFT code support
- Pluggable impurity solvers
- Flexible workflow customization
- Python-based scripting
DFT Code Integration:
- VASP interface (primary)
- Wannier90 for downfolding
- Support for other DFT codes via adapters
- Automated interface generation
- Seamless data exchange
Impurity Solver Support:
- ComCTQMC (included CT-HYB solver)
- Interface to external solvers
- Solver parameter optimization
- Multiple solver comparison
Charge Self-Consistency:
- Full DFT-DMFT charge self-consistency
- Iterative convergence algorithms
- Mixing schemes for stability
- Convergence monitoring and diagnostics
Inputs & Outputs
-
Input formats:
- Python configuration scripts
- VASP POSCAR and output files
- Wannier90 outputs
- DMFT parameter files
- Interaction parameters (U, J matrices)
-
Output data types:
- Self-energy functions
- Green's functions (local and k-resolved)
- Spectral functions and DOS
- Charge densities
- Magnetic moments
- Occupation matrices
- Convergence histories
- HDF5 data files
Interfaces & Ecosystem
-
DFT interfaces:
- VASP (primary backend)
- Wannier90 for projections
- Interface layer for other codes
-
Impurity solvers:
- ComCTQMC (built-in)
- TRIQS solvers (compatible)
- Custom solver interfaces
-
Analysis tools:
- Python post-processing scripts
- Spectral function analysis
- MaxEnt analytical continuation
- Visualization utilities
-
Workflow management:
- Python-based workflow scripts
- Automated job submission
- Checkpoint and restart
Workflow and Usage
Typical DFT+DMFT Workflow:
-
DFT Preparation:
- Run VASP DFT calculation
- Generate Wannier functions with Wannier90
- Define correlated orbitals
-
DMFT Setup:
- Configure ComDMFT parameters
- Set interaction parameters (U, J)
- Choose impurity solver and settings
- Define temperature and frequency grids
-
Self-Consistent Calculation:
- Initialize DMFT loop
- Iterate DFT and DMFT steps
- Solve impurity problem each iteration
- Update charge density
- Monitor convergence
-
Post-Processing:
- Extract spectral functions
- Perform analytical continuation
- Calculate observables
- Compare with experiments
Python Scripting Example:
# Example workflow structure
import comdmft
# Initialize calculation
calc = comdmft.DMFTCalculation(
dft_code='vasp',
structure='POSCAR',
correlated_orbitals=['d']
)
# Set parameters
calc.set_hubbard_u(U=5.0, J=0.7)
calc.set_temperature(T=300)
# Run DFT+DMFT
calc.run_self_consistent(max_iter=50)
# Analyze results
spectrum = calc.get_spectral_function()
Advanced Features
Orbital Selection:
- Flexible definition of correlated subspace
- Wannier-based downfolding
- Energy window selection
- Orbital character analysis
Interaction Parameters:
- Full Coulomb interaction matrix
- Kanamori parametrization
- Slater integrals
- Constrained RPA calculations
- Screening effects
Double Counting:
- Multiple schemes (FLL, AMF, etc.)
- Orbital-dependent corrections
- Self-consistent determination
Convergence Acceleration:
- Charge mixing algorithms
- DIIS extrapolation
- Anderson mixing
- Adaptive damping
Computational Aspects
Performance:
- CTQMC solver: computationally intensive
- Typical iteration: hours to days
- Full calculation: days to weeks
- MPI parallelization for solver
- Python overhead minimal
Memory Requirements:
- Moderate for DMFT framework
- Solver-dependent memory usage
- CTQMC most memory-intensive
- HDF5 for efficient data storage
Scalability:
- Good parallel scaling for CTQMC
- Multiple k-point parallelization
- Frequency parallelization possible
Limitations & Known Constraints
- Computational cost: DFT+DMFT very expensive
- VASP focus: Primarily designed for VASP
- Learning curve: Steep; requires DFT+DMFT knowledge
- Python dependency: Requires Python environment
- CTQMC limitations: Sign problem, statistical noise
- Analytical continuation: MaxEnt uncertainties
- Parameter dependence: Results sensitive to U, J, double counting
- System size: Limited by DFT and DMFT costs
- Documentation: Good but assumes expertise
- Platform: Linux/Unix
Application Areas
Transition Metal Oxides:
- Cuprates and nickelates
- Cobaltites and manganites
- Vanadates and titanates
- Correlation-driven phenomena
Strongly Correlated Materials:
- Mott insulators
- Heavy fermion systems
- Orbital ordering
- Magnetic phase transitions
Spectroscopy Simulation:
- Photoemission spectroscopy (ARPES)
- X-ray absorption (XAS/XMCD)
- Optical conductivity
- Transport properties
Materials Design:
- Screening correlated materials
- Property prediction
- Phase diagram exploration
Comparison with Other DMFT Frameworks
- vs TRIQS: ComDMFT more focused on VASP integration
- vs EDMFTF: Different DFT backend, similar capabilities
- vs w2dynamics: ComDMFT full DFT+DMFT framework vs solver only
- Unique strength: Python-based, modular architecture, VASP focus
Best Practices
Getting Started:
- Start with non-self-consistent calculation
- Validate with known systems
- Carefully converge parameters
- Document all settings
Parameter Selection:
- Validate U and J from literature or cRPA
- Test multiple double counting schemes
- Explore temperature dependence
- Check frequency grid convergence
Convergence:
- Use mixing for stability
- Monitor all observables
- Save checkpoints frequently
- Compare different solver settings
Validation:
- Compare with experiments
- Check sum rules
- Validate limiting cases
- Perform systematic studies
Community and Development
- Active development on GitHub
- Open-source contributions welcome
- Issue tracking and bug reports
- Community discussions
- Regular updates and improvements
Verification & Sources
Primary sources:
- GitHub repository: https://github.com/ComDMFT/ComDMFT
- Documentation: https://github.com/ComDMFT/ComDMFT/wiki
- ComCTQMC solver: https://github.com/ComDMFT/ComCTQMC
- Related publications from Comscope group
Secondary sources:
- ComDMFT tutorials and examples
- Workshop materials
- Published DFT+DMFT studies using ComDMFT
- 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 (GitHub)
- Documentation: ACCESSIBLE (Wiki)
- Source code: OPEN (GitHub, GPL v3)
- Community support: Active (GitHub issues, discussions)
- Academic citations: Growing user base
- Active development: Regular commits and updates
- Integration: Well-integrated with VASP and Wannier90
- Framework: Modern Python-based architecture