Python Tight Binding (PythTB)#

PythTB is a Python library for constructing and analyzing tight-binding models, built for modern topological band theory applications. It provides a streamlined path from model specification to physical interpretation, making it useful for both learning electronic structure and conducting research-level studies. With only a few lines of code, you can define lattice models, build tight-binding Hamiltonians, and compute electronic properties.

PythTB 2.0.0 Released!

Release Notes
Changelog

Quick Links

  • GitHub - source code and issue tracker

  • Installation - install instructions and dependencies

  • API - detailed API reference

  • Tutorials - Jupyter notebooks demonstrating key features

  • Development - contributing guidelines and developer docs

  • Release Notes - discussion of new features by version

  • Changelog - list of changes by version

  • Formalism - theoretical background

  • Citation - how to cite PythTB in publications

Core functionality#

Model construction

Construct and manipulate tight-binding Hamiltonians with TBModel. Define lattice geometry with Lattice, on-site and hopping terms, spin structure, and parameter-dependent model components.

pythtb.TBModel
State sampling

Create structured k-space and parameter meshes, sample model Hamiltonians, and store eigenstates in WFArray objects for downstream analysis.

pythtb.WFArray
Topology & quantum geometry

Compute topological invariants and quantum-geometric quantities with WFArray and TBModel: Berry connection, phases and curvature; quantum metric and geometric tensor; Wilson loops, Chern numbers, local Chern markers, hybrid Wannier centers, and Chern-Simons axion-angle pumping.

PythTB API Reference
Wannier90 Integration

Import Wannier90 tight-binding Hamiltonians via W90 for post-processing, band-structure evaluation, and detailed analysis of topological and quantum-geometric properties.

pythtb.W90
Wannier workflows

Perform projection, disentanglement, and construct maximally localized Wannier functions in a tight-binding framework using Wannier and WFArray. Evaluate spreads, centers, localization properties, and topological obstructions.

pythtb.Wannier
Visualization

Plot band structures, density of states, lattice geometries, hopping graphs, and interactive 3D models with built-in visualization utilities.

PythTB API Reference

Get started with PythTB#

This is a simple example showing how to define graphene tight-binding model with first neighbor hopping only. Below is the source code and plot of the resulting band structure. Here you can find more examples.

from pythtb.models import graphene
import matplotlib.pyplot as plt

# Create graphene TB model and visualize
model = graphene(delta=0, t=-1)
fig, ax = model.visualize()
plt.show()

# Plot band structure along high-symmetry points
nodes = [[0, 0], [2 / 3, 1 / 3], [1 / 2, 1 / 2], [0, 0]]
label = (r"$\Gamma $", r"$K$", r"$M$", r"$\Gamma $")
fig, ax = model.plot_bands(k_nodes=nodes, k_node_labels=label, nk=200)
plt.show()
_images/graphene_lattice.png

Graphene lattice#
_images/graphene_bands.png

Graphene band structure#

Feedback#

Please send comments or suggestions for improvement to these email addresses or start a discussion on the GitHub Discussions page. If you find bugs, please report them on the GitHub Issues page.