Pybinding is a Python package for numerical tight-binding calculations in solid state physics. The main features include:

• Declarative model construction - The user just needs to describe what the model should be, but not how to build it. Pybinding will take care of the numerical details of building the Hamiltonian matrix so users can concentrate on the physics: the quantum properties of the model.

• Fast compute - Pybinding's implementation of the kernel polynomial method allows for very fast calculation of the Green's function of the Hamiltonian. Exact diagonalization is also available through the use of scipy's eigensolvers. The framework is very flexible and allows the addition of user-defined computation routines.

• Result analysis and visualization - The package contains utility functions for post-processing the raw result data. The included plotting functions are tailored for tight-binding problems to help visualize the model structure and make sense of the results.

The code interface is written in Python with the aim to be as user-friendly and flexible as possible. Under the hood, C++11 is used to accelerate demanding tasks to deliver high performance with low memory usage.

See the documentation for more details.

Pybinding can be installed on Windows, Linux or Mac, with the following prerequisites:

• Python 3.4 or newer (Python 2.x is not supported)
• The SciPy stack of scientific packages, with required versions:
  • numpy >= v1.9
  • scipy >= v0.15
  • matplotlib >= v1.5
• If you're using Linux, you'll also need GCC >= v4.8 and CMake >= v3.0.

Detailed install instructions are part of the documentation, but if you already have all the prerequisites, it's just a simple case of using pip, Python's usual package manager:

pip install pybinding

Work in progress. The goal of the project is to develop a tight-binding code framework which is fast, flexible and easy to use. See the documentation for more details and a guide to get started.

• Construction of arbitrary tight-binding lattices and geometries: 1 to 3 dimensions (including multilayer 2D systems), periodic or finite size (with fine control of edges)
• Easy polygon shape definition for 2D systems and freeform shapes for n-dimensional systems
• Geometric deformations and defects: defined via displacement and state functions
• Fields and arbitrary effects: defined via hopping and onsite energy functions
• Green's function: fast kernel polynomial method implementation
• Eigensolvers: standard dense and sparse solvers (always available) and the FEAST solver (only available when compiled with Intel's MKL)
• Model and result objects have builtin plotting functions for easy visualization

• Support for transport calculations
• Improvements for 3D systems (mostly related to plotting functions)
• Multiple orbitals and spins are already supported, but could use a nicer interface

If you have any questions, feel free to join the chat room on Gitter. You can also open an issue at the tracker.