Official Resources
- Homepage: http://www.paoflow.org/
- Documentation: http://www.paoflow.org/
- Source Repository: https://github.com/marcobn/PAOFLOW
- License: GNU General Public License v3.0
Overview
PAOFLOW is a Python-based post-processing tool for electronic structure calculations, designed to compute transport, topological, and optical properties from DFT calculations using tight-binding models constructed with atomic orbital projections. Developed primarily at the University of North Texas, PAOFLOW provides automated workflows for property calculations from first principles with emphasis on ease of use and comprehensive analysis capabilities.
Scientific domain: Electronic transport, topological properties, DFT post-processing
Target user community: DFT users, transport calculations, materials properties
Theoretical Methods
- Projections of atomic orbitals
- Tight-binding from DFT
- Boltzmann transport
- Berry phase properties
- Topological invariants
- Optical conductivity
- Spin textures
Capabilities (CRITICAL)
Category: Open-source DFT post-processing tool
- DFT to TB conversion (atomic projections)
- Band structure interpolation
- Boltzmann transport (σ, S, κₑ)
- Berry curvature and AHC
- Topological invariants (Z2, Chern)
- Optical properties
- Spin Hall conductivity
- Spin textures
- DFT interface (Quantum ESPRESSO, others)
- Python implementation
- Automated workflows
- Production quality
Sources: Official website, GitHub, publications
Key Strengths
Comprehensive Properties:
- Transport coefficients
- Topological invariants
- Optical properties
- Berry phase phenomena
- Spin properties
- All-in-one tool
Automated Workflows:
- DFT to properties
- Minimal user intervention
- Standard pipelines
- Production ready
- User-friendly
Python-Based:
- Accessible interface
- NumPy/SciPy
- Visualization tools
- Extensible
- Modern design
Atomic Projections:
- PAO-based TB construction
- DFT integration
- No Wannier90 required
- Alternative approach
- Flexible
Inputs & Outputs
-
Input formats:
- DFT outputs (QE primarily)
- Atomic orbital projections
- Configuration files
-
Output data types:
- Transport coefficients
- Topological invariants
- Band structures
- Optical conductivity
- Spin textures
- Berry curvature maps
Interfaces & Ecosystem
DFT Codes:
- Quantum ESPRESSO (primary)
- Extensible to others
- Atomic projections
- Standard workflow
Visualization:
- Built-in plotting
- Matplotlib integration
- Publication-ready
- Property maps
Workflow and Usage
Installation:
# Via pip
pip install paoflow
# From source
git clone https://github.com/marcobn/PAOFLOW.git
cd PAOFLOW
python setup.py install
Basic Workflow:
from PAOFLOW import PAOFLOW
# Initialize
paoflow = PAOFLOW(
savedir='./paoflow_output',
verbose=True
)
# Read DFT data (Quantum ESPRESSO)
paoflow.read_atomic_proj_QE()
# Build Hamiltonian
paoflow.projectability()
paoflow.pao_hamiltonian()
# Band structure
paoflow.bands(
ibrav=2,
nk=1000,
band_path=['L', 'G', 'X']
)
# Transport properties
paoflow.transport(
tmin=100, # K
tmax=800,
tstep=50
)
# Topological properties
paoflow.z2_pack()
paoflow.Berry_curvature()
paoflow.anomalous_Hall()
# Optical properties
paoflow.optical_conductivity()
# Generate output
paoflow.finish_execution()
Quantum ESPRESSO Interface:
# 1. SCF calculation
pw.x < scf.in > scf.out
# 2. NSCF with projections
pw.x < nscf.in > nscf.out
# 3. Projections
projwfc.x < proj.in > proj.out
# 4. PAOFLOW processing
python paoflow_script.py
Advanced Features
Transport:
- Electrical conductivity
- Seebeck coefficient
- Electronic thermal conductivity
- Power factor
- Temperature dependence
- Boltzmann equation
Topological:
- Z2 invariants
- Chern numbers
- Berry curvature
- Anomalous Hall conductivity
- Topological characterization
Optical:
- Optical conductivity
- Dielectric function
- Absorption
- Interband transitions
- Frequency-dependent
Spin:
- Spin Hall conductivity
- Spin textures
- Spin-orbit effects
- Rashba/Dresselhaus
Performance Characteristics
- Speed: Fast post-processing
- Accuracy: DFT quality
- Purpose: Comprehensive properties
- Typical: Minutes to hours
Computational Cost
- Post-DFT processing
- DFT calculation dominant
- Property calculations fast
- Production capable
- Automated
Limitations & Known Constraints
- Requires DFT: Post-processing tool
- PAO projections: Alternative to Wannier
- QE focus: Primarily Quantum ESPRESSO
- Python speed: Moderate for large systems
- Documentation: Growing
Comparison with Other Tools
- vs Wannier90 ecosystem: PAOFLOW PAO-based, W90 MLWF-based
- vs BoltzTraP: PAOFLOW comprehensive, BoltzTraP transport specialist
- vs WannierBerri: Different projection approach
- Unique strength: All-in-one DFT post-processing, automated workflows, PAO projections
Application Areas
Thermoelectrics:
- Transport coefficients
- Material screening
- Doping optimization
- ZT estimation
Topological Materials:
- Topological classification
- Berry phase properties
- Anomalous Hall
- Material discovery
Optical Properties:
- Absorption spectra
- Optical conductivity
- Dielectric response
- Spectroscopy theory
Spintronics:
- Spin Hall effect
- Spin textures
- SOC materials
- Device applications
Best Practices
DFT Input:
- Quality SCF/NSCF
- Appropriate projections
- Sufficient k-points
- Converged calculations
Workflows:
- Follow examples
- Standard pipelines
- Property selection
- Validation
Analysis:
- Visualize results
- Physical interpretation
- Compare with experiment
- Systematic studies
Community and Support
- Open-source (GPL v3)
- University of North Texas
- GitHub repository
- Documentation website
- Active development
- User community
Educational Resources
- Official documentation
- Tutorial examples
- Example gallery
- Publication list
- Workflow guides
Development
- Marco Buongiorno Nardelli (lead)
- University of North Texas
- Active development
- Regular updates
- Community contributions
Research Impact
PAOFLOW provides comprehensive electronic structure property calculations from DFT, enabling systematic materials screening for transport, topological, and optical applications.
Verification & Sources
Primary sources:
- Homepage: http://www.paoflow.org/
- GitHub: https://github.com/marcobn/PAOFLOW
- Publications: Comp. Phys. Comm. 235, 415 (2019)
Secondary sources:
- User publications
- Materials property papers
Confidence: VERIFIED - DFT post-processing tool
Verification status: ✅ VERIFIED
- Website: ACTIVE
- GitHub: ACCESSIBLE
- License: GPL v3 (open-source)
- Category: DFT post-processing tool
- Status: Actively developed
- Institution: University of North Texas
- Specialized strength: Comprehensive property calculations from DFT using atomic orbital projections, transport (Boltzmann), topological invariants, optical properties, spin textures, automated workflows, Python-based, all-in-one post-processing, production quality, alternative to Wannier-based approaches