effmass

Official Resources

• Homepage: https://effmass.readthedocs.io/
• GitHub: https://github.com/lucydot/effmass
• Documentation: https://effmass.readthedocs.io/
• PyPI: https://pypi.org/project/effmass/
• Publication: L. Whalley et al., J. Open Source Softw. 3, 797 (2018)
• License: MIT License

Overview

effmass is a Python package for calculating effective masses from ab initio band structure calculations. It provides tools for extracting carrier effective masses using various models including parabolic approximation and Kane's non-parabolic model. The package automatically identifies band extrema and handles multiple valleys.

Scientific domain: Semiconductor physics, effective mass calculation, carrier transport Target user community: Researchers studying semiconductors, thermoelectrics, and carrier transport properties

Theoretical Background

effmass implements effective mass calculations based on:

• Parabolic approximation: m* = ℏ²/(d²E/dk²)
• Kane model for non-parabolic bands: E(1 + E/Eg) = ℏ²k²/2m*
• Optical effective mass: 1/m*_opt = 1/m*_e + 1/m*_h
• DOS effective mass: m*_DOS = (m*_1 · m*_2 · m*_3)^(1/3)
• Conductivity effective mass

Capabilities (CRITICAL)

• Effective Mass Calculation: From DFT band structures
• Parabolic Fitting: Standard band curvature analysis
• Non-parabolic Bands: Kane model for narrow-gap semiconductors
• Multi-valley: Handle degenerate and multiple band extrema
• Automatic Detection: Find VBM/CBM automatically
• Optical Mass: Combined electron-hole effective mass
• DOS Mass: Density of states effective mass
• Anisotropy: Direction-dependent effective masses

Key Strengths

Multiple Models:

• Parabolic approximation
• Kane non-parabolic model
• Polynomial fitting
• Finite difference methods

Automatic Analysis:

• Band extrema detection
• Valley identification
• Degeneracy handling
• Energy window selection

Multi-Code Support:

• VASP (vasprun.xml)
• FHI-aims
• CASTEP
• Extensible to other codes

Publication Quality:

• Matplotlib visualization
• Data export
• Reproducible analysis

Inputs & Outputs

• Input formats:

  • vasprun.xml (VASP)
  • FHI-aims band output
  • CASTEP band files

• Output data types:

  • Effective mass values
  • Fitting parameters
  • Band curvature data
  • Matplotlib figures

Interfaces & Ecosystem

• Python integration:
  • NumPy for numerical operations
  • SciPy for fitting
  • Matplotlib for visualization
  • pymatgen for structure handling

Installation

pip install effmass

Usage Examples

from effmass import inputs, analysis, outputs

# Load data
settings = inputs.Settings(extrema_search_depth=0.025)
data = inputs.DataVasp(vasprun="vasprun.xml", settings=settings)

# Find segments around band edges
segments = analysis.generate_segments(data)

# Calculate effective masses
for segment in segments:
    print(f"Effective mass: {segment.effective_mass}")
    
# Plot
outputs.plot_segments(data, segments)

Performance Characteristics

• Speed: Fast band analysis
• Accuracy: Multiple fitting methods
• Robustness: Handles complex band structures

Limitations & Known Constraints

• k-point density: Requires dense k-point sampling
• Band crossing: May struggle with complex crossings
• Spin-orbit: Requires careful handling
• Indirect gaps: Need appropriate k-path

Comparison with Other Tools

• vs effectivemass (AFLOW): effmass has more fitting models
• vs manual fitting: effmass is automated, reproducible
• vs sumo: effmass specialized for effective mass
• Unique strength: Kane model, automatic extrema detection

Application Areas

• Semiconductor characterization
• Thermoelectric materials
• Solar cell materials
• Transport property prediction
• Band engineering
• Carrier mobility estimation

Best Practices

• Use dense k-point grids near band edges
• Check fitting quality visually
• Compare parabolic and non-parabolic results
• Consider anisotropy for accurate transport
• Validate against experimental data

Community and Support

• GitHub issue tracker
• JOSS publication for citation
• Documentation with examples
• Active maintenance

Verification & Sources

Primary sources:

1. Official documentation: https://effmass.readthedocs.io/
2. GitHub repository: https://github.com/lucydot/effmass
3. L. Whalley et al., J. Open Source Softw. 3, 797 (2018)

Confidence: VERIFIED - Published in JOSS

Verification status: ✅ VERIFIED

• Official homepage: ACCESSIBLE
• Documentation: COMPREHENSIVE
• Source code: OPEN (GitHub, MIT)
• Developer: Lucy Whalley
• Academic citations: JOSS publication
• Active development: Maintained