IFermi

Official Resources

• Homepage: https://fermisurfaces.github.io/IFermi/
• GitHub: https://github.com/fermisurfaces/IFermi
• Documentation: https://fermisurfaces.github.io/IFermi/
• PyPI: https://pypi.org/project/ifermi/
• Publication: A. Ganose et al., J. Open Source Softw. 6, 3089 (2021)
• License: MIT License

Overview

IFermi is a Python package for Fermi surface generation, analysis, and visualization from ab initio calculations. It provides tools for extracting Fermi surfaces from DFT band structures, computing Fermi surface properties, and creating publication-quality 2D and 3D visualizations. Developed by the Materials Project team, it integrates seamlessly with pymatgen.

Scientific domain: Fermi surface analysis, electronic structure visualization, metal physics Target user community: Researchers studying metallic systems, superconductors, topological materials, and transport properties

Theoretical Background

IFermi analyzes Fermi surfaces based on:

• Fermi surface: Constant energy surface at E = E_F in k-space
• Isosurface extraction using marching cubes algorithm
• Brillouin zone integration for properties
• Spin texture from spin-polarized calculations
• Fermi velocity: v_F = (1/ℏ)∇_k E(k)

Capabilities (CRITICAL)

• Fermi Surface Generation: Extract isosurfaces from band structures
• 2D Slices: Fermi surface cross-sections along arbitrary planes
• 3D Visualization: Interactive 3D Fermi surface plots
• Spin Textures: Spin-resolved Fermi surfaces and spin projections
• Property Calculation: Fermi velocities, areas, volumes
• Interpolation: Band structure interpolation for smooth surfaces
• Multi-band: Handle multiple bands crossing Fermi level
• Export: Save surfaces in various formats

Key Strengths

Visualization Options:

• Mayavi for high-quality 3D rendering
• Plotly for interactive web-based plots
• Matplotlib for 2D slices and publication figures
• Customizable colors and projections

Analysis Capabilities:

• Fermi surface area calculation
• Average Fermi velocity
• Density of states at Fermi level
• Spin texture analysis
• Band-resolved properties

Multi-Code Support:

• VASP (vasprun.xml, native)
• Quantum ESPRESSO (via BoltzTraP2)
• BXSF format (universal)
• Extensible to other codes

Integration:

• pymatgen compatibility
• BoltzTraP2 interface
• Command-line and Python API
• Jupyter notebook friendly

Inputs & Outputs

• Input formats:

  • vasprun.xml (VASP)
  • BXSF files (XCrySDen format)
  • BoltzTraP2 interpolated bands
  • pymatgen BandStructure objects

• Output data types:

  • 3D Fermi surface meshes
  • 2D slice images
  • Property data (velocities, areas)
  • Interactive HTML plots
  • Image files (PNG, SVG, PDF)

Interfaces & Ecosystem

• Python integration:

  • pymatgen for structure/band handling
  • NumPy/SciPy for numerical operations
  • trimesh for mesh operations
  • matplotlib, plotly, mayavi for visualization

• Framework compatibility:

  • Materials Project workflows
  • atomate workflows
  • Jupyter notebooks
  • High-throughput screening

Installation

pip install ifermi

With all visualization backends:

pip install ifermi[mayavi,plotly]

Usage Examples

Command line:

# Plot 3D Fermi surface
ifermi plot vasprun.xml

# Plot 2D slice along (001) plane
ifermi plot --slice 0 0 1 vasprun.xml

# Calculate properties
ifermi info vasprun.xml

Python API:

from ifermi.surface import FermiSurface
from ifermi.interpolate import FourierInterpolator
from pymatgen.io.vasp import Vasprun

# Load band structure
vr = Vasprun("vasprun.xml")
bs = vr.get_band_structure()

# Interpolate and generate Fermi surface
interpolator = FourierInterpolator(bs)
new_bs, velocities = interpolator.interpolate_bands(return_velocities=True)
fs = FermiSurface.from_band_structure(new_bs, velocities=velocities)

# Plot
fs.plot()

Performance Characteristics

• Speed: Fast isosurface extraction
• Memory: Efficient mesh handling
• Interpolation: Fourier interpolation for smooth surfaces
• Scalability: Handles complex multi-sheet Fermi surfaces

Limitations & Known Constraints

• Metallic systems only: Requires bands crossing Fermi level
• Interpolation quality: Depends on k-point density
• Spin-orbit: Requires careful handling
• Visualization: 3D backends may need additional setup
• Large systems: Memory for very fine meshes

Comparison with Other Tools

• vs FermiSurfer: IFermi is Python-native, better integration
• vs XCrySDen: IFermi has more analysis features
• vs VESTA: IFermi specialized for Fermi surfaces
• Unique strength: pymatgen integration, spin textures, property calculation

Application Areas

• Metallic electronic structure
• Superconductivity studies
• Topological material analysis
• Transport property understanding
• Fermi surface nesting analysis
• de Haas-van Alphen effect interpretation
• ARPES comparison

Best Practices

• Use dense k-point grids for smooth surfaces
• Interpolate bands for better resolution
• Check multiple bands near Fermi level
• Verify with experimental data when available
• Use appropriate energy window for interpolation

Community and Support

• GitHub issue tracker
• Materials Project community
• JOSS publication for citation
• Active development

Verification & Sources

Primary sources:

1. Official documentation: https://fermisurfaces.github.io/IFermi/
2. GitHub repository: https://github.com/fermisurfaces/IFermi
3. A. Ganose et al., J. Open Source Softw. 6, 3089 (2021)

Confidence: VERIFIED - Published in JOSS, Materials Project team

Verification status: ✅ VERIFIED

• Official homepage: ACCESSIBLE
• Documentation: COMPREHENSIVE
• Source code: OPEN (GitHub, MIT)
• Developer: Materials Project team (A. Ganose et al.)
• Academic citations: JOSS publication
• Active development: Regular releases