Official Resources
- Source Repository: https://github.com/SMTG-Bham/doped
- Documentation: https://doped.readthedocs.io/
- PyPI: https://pypi.org/project/doped/
- License: MIT License
Overview
doped is a Python software for the generation, simulation, and analysis of defect supercells in solid-state materials. It automates the workflow for point defect calculations using VASP and pymatgen, including symmetry analysis, finite-size corrections, and defect property analysis.
Scientific domain: Defect calculations, point defect simulation, defect analysis
Target user community: Researchers studying point defects in semiconductors, insulators, and functional materials
Theoretical Methods
- Point defect thermodynamics
- Formation energy calculation
- Symmetry analysis of defects
- Finite-size corrections (Kumagai-Oba eFNV, Freysoldt)
- Chemical potential determination
- Fermi level self-consistency
- Charge state analysis
- VASP DFT backend
Capabilities (CRITICAL)
- Automated defect supercell generation
- Symmetry-inequivalent defect identification
- VASP input file generation
- Competing phase analysis for chemical potentials
- Formation energy calculation
- Finite-size charge correction (eFNV, Freysoldt)
- Defect concentration calculation
- Fermi level vs temperature analysis
- Carrier concentration analysis
- Plotting and analysis tools
Sources: GitHub repository, JOSS publication
Key Strengths
Automated Workflow:
- End-to-end defect calculation workflow
- Symmetry analysis reduces redundant calculations
- Automatic VASP input generation
- Competing phase determination
Comprehensive Corrections:
- Freysoldt (FNV) correction
- Kumagai-Oba (eFNV) correction
- Anisotropic dielectric screening
- Band gap alignment
Rich Analysis:
- Formation energy diagrams
- Concentration vs temperature
- Fermi level determination
- Carrier concentration plots
Pymatgen Integration:
- Built on pymatgen
- Materials Project API
- Standard file formats
- Extensible framework
Inputs & Outputs
-
Input formats:
- pymatgen Structure objects
- VASP output files
- Chemical potential data
-
Output data types:
- VASP input files for defect calculations
- Formation energy diagrams
- Defect concentrations
- Fermi level vs temperature
- Charge correction plots
Interfaces & Ecosystem
- VASP: Primary DFT backend
- pymatgen: Materials analysis framework
- Materials Project: Database access
- Matplotlib: Visualization
Performance Characteristics
- Speed: Fast (workflow management)
- Accuracy: DFT-level for defect properties
- System size: Limited by VASP supercell
- Automation: Full workflow automation
Computational Cost
- Workflow setup: Seconds
- VASP calculations: Hours to days (separate)
- Analysis: Seconds
- Typical: Efficient workflow, VASP is bottleneck
Limitations & Known Constraints
- VASP only: No QE or other code support
- Point defects only: No extended defects
- DFT-level: No GW or hybrid corrections built-in
- Supercell approach: Finite-size effects remain
Comparison with Other Codes
- vs pymatgen-analysis-defects: doped is more automated and VASP-focused
- vs C2DB defect workflow: doped is general, C2DB is 2D-specific
- vs PyCDT: doped is newer with better corrections
- Unique strength: End-to-end automated defect calculation workflow with VASP, comprehensive corrections and analysis
Application Areas
Semiconductors:
- Native defect formation energies
- Dopant incorporation
- Carrier concentration prediction
- Fermi level engineering
Photovoltaics:
- Defect tolerance analysis
- Recombination-active defects
- Doping optimization
- Stability assessment
Battery Materials:
- Cation disorder
- Oxygen vacancy formation
- Electrolyte decomposition
- Degradation mechanisms
Functional Oxides:
- Oxygen vacancy concentrations
- Redox-active defects
- Conductivity prediction
- Phase stability
Best Practices
Supercell Selection:
- Use sufficiently large supercells
- Check convergence with supercell size
- Consider charge state effects
- Validate finite-size corrections
Chemical Potentials:
- Include all competing phases
- Check stability region
- Use consistent DFT settings
- Consider off-stoichiometry
Analysis:
- Use appropriate charge corrections
- Consider all charge states
- Include metastable states
- Compare with experiment
Community and Support
- Open source (MIT License)
- PyPI installation available
- ReadTheDocs documentation
- Developed at University of Birmingham (SMTG)
- Active development
Verification & Sources
Primary sources:
- GitHub: https://github.com/SMTG-Bham/doped
- Documentation: https://doped.readthedocs.io/
- M. K. Horton et al., JOSS (2024)
Confidence: VERIFIED
Verification status: ✅ VERIFIED
- Source code: ACCESSIBLE (GitHub)
- Documentation: ACCESSIBLE (ReadTheDocs)
- PyPI: AVAILABLE
- Active development: Ongoing
- Specialized strength: End-to-end automated defect calculation workflow with VASP, comprehensive corrections and analysis