Quickstart

This page walks you through the Silicon electronic structure workflow — bands, DOS, and transport — using pre-computed QE data that ships with PAOFLOW.

---

What you will do

1. Place the pre-computed silicon.save/ directory in your working directory

2. Run a short Python script that builds the PAO Hamiltonian from the QE output

3. Inspect the output files for band structure, density of states, and transport tensors

You do not need to run Quantum ESPRESSO yourself. The silicon.save/ directory contains the required atomic_proj.xml and data-file-schema.xml outputs.

---

Prerequisites

PAOFLOW installed (see Installation):

pip install PAOFLOW

---

Step 1 — Obtain the tutorial assets

Download the precomputed tutorial assets from the PAOFLOW Releases page and extract them into a working directory. The assets include:

silicon.save/          # Pre-computed QE output (atomic projections + data)

---

Step 2 — Examine the driver script

Open main.py. It shows the standard PAOFLOW Python API pattern:

from PAOFLOW import PAOFLOW

def main():
    # Initialise PAOFLOW from a QE .save directory
    paoflow = PAOFLOW.PAOFLOW(
        savedir='silicon.save',
        outputdir='output',
        smearing='gauss',
        npool=1,
        verbose=True,
    )

    # Read pre-computed atomic projections from QE
    paoflow.read_atomic_proj_QE()

    # Drop bands with low projectability onto the PAO basis
    paoflow.projectability()

    # Construct the real-space Hamiltonian H(R)
    paoflow.pao_hamiltonian()

    # Interpolate onto the band path for ibrav=2 (FCC, e.g. Si)
    paoflow.bands(ibrav=2, nk=2000)

    # Double the Monkhorst-Pack grid by Fourier interpolation
    paoflow.interpolated_hamiltonian()

    # Diagonalise H(k) on the full BZ grid
    paoflow.pao_eigh()

    # Compute momentum matrix elements (needed for transport/optics)
    paoflow.gradient_and_momenta()

    # Apply adaptive Gaussian smearing for BZ integration
    paoflow.adaptive_smearing()

    # Compute DOS and transport tensors
    paoflow.dos(emin=-12., emax=2.2, ne=1000)
    paoflow.transport(emin=-12., emax=2.2)

    paoflow.finish_execution()

if __name__ == '__main__':
    main()

---

Step 3 — Run PAOFLOW

python main.py

For a parallel run with MPI (optional, speeds up dense k-grids):

mpirun -np 4 python main.py

The run typically completes in under a minute on a laptop for this small example.

---

Step 4 — Check the output

After the run, an output/ directory is created containing:

File	Contents
bands_0.dat	Band structure along the default FCC k-path
dosdk_0.dat	Total density of states
sigmagauss_0.dat	Electrical conductivity tensor vs. energy
Seebeckgauss_0.dat	Seebeck coefficient tensor vs. energy
kappagauss_0.dat	Electronic thermal conductivity tensor vs. energy

Tip

Comparing to the reference The Reference/ subdirectory contains expected output files. You can diff your results against them to confirm a correct installation.

---

Next steps

• Explore the Tutorials for in-depth guided workflows covering more advanced topics (spin–orbit coupling, topology, transport).

• Use the paoflow-gen command-line tool to generate a driver script for your own QE calculation:

  paoflow-gen
  

• See the API Reference for the full list of methods on the PAOFLOW class.