postqe

Official Resources

• Homepage: https://github.com/QEF/postqe
• GitHub: https://github.com/QEF/postqe
• Documentation: https://postqe.readthedocs.io/
• PyPI: https://pypi.org/project/postqe/
• License: GPL v2

Overview

postqe is a Python package for post-processing calculations from Quantum ESPRESSO, developed by the Quantum ESPRESSO Foundation. It provides functions to analyze and visualize results including charge density, potentials, electronic structure, and equation of state fitting.

Scientific domain: Quantum ESPRESSO post-processing, electronic structure visualization Target user community: Quantum ESPRESSO users requiring Python-based analysis

Theoretical Background

postqe processes Quantum ESPRESSO output to analyze:

• Charge density ρ(r) on 1D/2D sections
• Electrostatic potentials (bare, Hartree, total)
• Kohn-Sham eigenvalues and band structures
• Equation of state E(V) fitting

Capabilities (CRITICAL)

• Charge Density: Plot on 1D/2D sections through crystal
• Potentials: Bare/Hartree/total potential visualization
• EOS Fitting: Murnaghan, Birch-Murnaghan equations of state
• Band Structure: Electronic band plotting
• DOS: Density of states analysis
• XML Parsing: Read QE XML output files

Key Strengths

Native QE Support:

• Direct XML file parsing
• All QE output types supported
• Official QEF development

Visualization:

• 1D line plots through structures
• 2D contour plots
• Matplotlib integration
• Publication-quality figures

EOS Analysis:

• Multiple EOS models
• Bulk modulus extraction
• Equilibrium volume fitting

Inputs & Outputs

• Input formats:

  • QE XML output files
  • Charge density files
  • Potential files
  • Band structure data

• Output data types:

  • Matplotlib figures
  • Numerical data arrays
  • Fitted parameters

Installation

pip install postqe

Usage Examples

from postqe import get_charge, plot_charge_1d

# Read charge density
charge = get_charge("charge-density.dat")

# Plot 1D section
plot_charge_1d(charge, [0,0,0], [1,0,0])

# EOS fitting
from postqe import fit_eos
volumes, energies = [...], [...]
params = fit_eos(volumes, energies, eos='birch-murnaghan')

Performance Characteristics

• Speed: Efficient file parsing
• Memory: Handles large charge density files
• Visualization: Fast matplotlib rendering

Limitations & Known Constraints

• QE-specific: Only for Quantum ESPRESSO output
• XML format: Requires QE XML output enabled
• Limited features: Focused on basic post-processing

Comparison with Other Tools

• vs abipy: postqe for QE, abipy for ABINIT
• vs pymatgen: postqe more QE-specific
• Unique strength: Official QEF tool, EOS fitting

Application Areas

• Charge density analysis
• Potential visualization
• Equation of state studies
• Band structure plotting
• Basic QE post-processing

Verification & Sources

Primary sources:

1. GitHub: https://github.com/QEF/postqe

Confidence: VERIFIED - Official QEF tool

Verification status: ✅ VERIFIED

• Source code: OPEN (GitHub, GPL v2)
• Developer: QEF (Quantum ESPRESSO Foundation)