Official Resources
- Homepage: https://github.com/GkAntonius/ElectronPhononCoupling
- Source Repository: https://github.com/GkAntonius/ElectronPhononCoupling
- License: GPL-3.0
Overview
ElectronPhononCoupling is a Python module for analyzing electron-phonon coupling quantities computed with Abinit. It provides tools for computing temperature-dependent band structures, zero-point renormalization, and other electron-phonon related properties.
Scientific domain: Electron-phonon coupling, temperature-dependent electronic structure
Target user community: Abinit users studying electron-phonon interactions
Theoretical Methods
- Allen-Heine-Cardona theory
- Temperature-dependent band gaps
- Zero-point renormalization (ZPR)
- Fan-Migdal self-energy
- Debye-Waller contributions
- Electron-phonon matrix elements
Capabilities (CRITICAL)
- Temperature-dependent band structures
- Zero-point renormalization
- Band gap temperature dependence
- Electron-phonon self-energy
- Fan and Debye-Waller terms
- Spectral functions
- Integration with Abinit output
Key Strengths
Abinit Integration:
- Direct use of Abinit output
- DFPT electron-phonon data
- Consistent methodology
- Well-tested workflow
Temperature Effects:
- Full temperature dependence
- Zero-point motion
- Quantum effects
- Accurate predictions
Inputs & Outputs
-
Input formats:
- Abinit netCDF files
- Electron-phonon matrix elements
- Phonon frequencies
-
Output data types:
- Temperature-dependent bands
- Renormalized gaps
- Self-energies
- Spectral functions
Interfaces & Ecosystem
- Abinit: Primary DFT code
- Python: Analysis framework
- abipy: Compatible tools
Advanced Features
- Allen-Heine-Cardona theory: Complete temperature dependence
- Zero-point renormalization: Quantum effects on band gaps
- Fan-Migdal self-energy: Electron-phonon coupling contributions
- Debye-Waller terms: Lattice vibration effects
- Spectral functions: Full energy-dependent analysis
- Abinit netCDF: Direct parsing of Abinit output
Performance Characteristics
- Post-processing tool: Moderate speed
- Depends on k-point and q-point grids
- Python-based implementation
Computational Cost
- Abinit DFPT: Dominant cost (external)
- EPC analysis: Minutes to hours
- Scales with system size and grid density
- Overall: DFPT calculations dominate
Best Practices
- Converge k-point and q-point grids
- Validate against experimental band gap temperature dependence
- Check Fan and Debye-Waller contributions separately
- Use appropriate smearing for spectral functions
Limitations & Known Constraints
- Abinit-specific
- Requires DFPT calculations
- Python expertise needed
- Limited documentation
Application Areas
- Semiconductor band gaps
- Temperature-dependent properties
- Superconductivity studies
- Optical properties
Verification & Sources
Primary sources:
- GitHub: https://github.com/GkAntonius/ElectronPhononCoupling
- G. Antonius et al., Phys. Rev. B 92, 085137 (2015)
Confidence: VERIFIED
Verification status: ✅ VERIFIED
- Source code: OPEN (GitHub, GPL-3.0)