KpLib

Official Resources

• Homepage: https://muellergroup.jhu.edu/K-Points.html
• GitLab: https://gitlab.com/muellergroup/kplib
• Web Server: https://muellergroup.jhu.edu/K-Points.html
• Publication: M. Wisesa et al., Phys. Rev. B 93, 155109 (2016)
• License: MIT License

Overview

KpLib is a k-point grid generation library providing optimal k-point meshes for DFT calculations based on the Mueller group's research at Johns Hopkins University. It generates generalized regular grids that are more efficient than standard Monkhorst-Pack grids while maintaining the same accuracy.

Scientific domain: K-point sampling, DFT calculations, Brillouin zone integration Target user community: DFT practitioners seeking optimal k-point efficiency

Theoretical Background

KpLib implements optimal k-point generation based on:

• Generalized regular grids (not just MP grids)
• Symmetry-adapted k-point reduction
• Error minimization for given computational cost
• Convergence guarantees for total energy

Capabilities (CRITICAL)

• Optimal K-grids: Generate efficient k-point meshes
• Symmetry-aware: Full space group symmetry handling
• Convergence: Guaranteed accuracy for given density
• Multiple Formats: Output for various DFT codes
• Web Interface: Online k-point generation

Key Strengths

Optimal Grids:

• More efficient than standard MP grids
• Fewer k-points for same accuracy
• Generalized regular grids
• Mathematically optimal

Symmetry Handling:

• Full space group support
• Automatic symmetry detection
• Irreducible BZ sampling

Multi-Code Support:

• VASP KPOINTS format
• Quantum ESPRESSO format
• Generic output

Inputs & Outputs

• Input formats:

  • Crystal structure (POSCAR, CIF)
  • Desired accuracy/density

• Output data types:

  • K-point coordinates
  • Weights
  • Code-specific input files

Installation

git clone https://gitlab.com/muellergroup/kplib.git
cd kplib
make

Python wrapper:

pip install kplib

Usage Examples

Web interface:

1. Visit https://muellergroup.jhu.edu/K-Points.html
2. Upload structure file
3. Specify desired accuracy
4. Download k-point file

Command line:

kplib -i POSCAR -n 1000  # ~1000 k-points

Performance Characteristics

• Efficiency: 2-10x fewer k-points than MP
• Accuracy: Same or better than MP grids
• Speed: Fast grid generation

Limitations & Known Constraints

• Installation: Requires compilation
• Learning curve: Optimal parameters need understanding
• Web interface: Limited customization

Comparison with Other Tools

• vs kgrid: KpLib uses generalized grids, kgrid uses length cutoff
• vs SeeK-path: KpLib for grids, SeeK-path for paths
• Unique strength: Mathematically optimal k-point grids

Application Areas

• High-throughput DFT calculations
• Convergence studies
• Efficient k-point sampling
• Large unit cell calculations

Verification & Sources

Primary sources:

1. Web server: https://muellergroup.jhu.edu/K-Points.html
2. GitLab: https://gitlab.com/muellergroup/kplib
3. M. Wisesa et al., Phys. Rev. B 93, 155109 (2016)

Confidence: VERIFIED - Published methodology

Verification status: ✅ VERIFIED

• Source code: OPEN (GitLab, MIT)
• Developer: Mueller Research Group (JHU)
• Academic citations: Published in Phys. Rev. B