NanoNET: an extendable Python framework for semi-empirical tight-binding models

TL;DR

NanoNET is an open-source Python framework that simplifies modeling electronic structure and transport in nanoscale materials using tight-binding and Green's function methods, supporting efficient Hamiltonian construction and analysis.

Contribution

It introduces a flexible, extendable Python toolkit with novel algorithms for Hamiltonian matrix construction, structure detection, and transport property calculations for nanoscale systems.

Findings

Successfully computed band structures of silicon nanowires and bulk bismuth.

Demonstrated efficient Hamiltonian construction from atomic coordinates.

Validated transport property calculations with example nanoscale systems.

Abstract

We present a novel open-source Python framework called NanoNET (Nanoscale Non-equilibrium Electron Transport) for modelling electronic structure and transport. Our method is based on the tight-binding method and non-equilibrium Green's function theory. The core functionality of the framework is providing facilities for efficient construction of tight-binding Hamiltonian matrices from a list of atomic coordinates and a lookup table of the two-center integrals in dense, sparse, or block-tridiagonal forms. The framework implements a method based on $kd$-tree nearest-neighbour search and is applicable to isolated atomic clusters and periodic structures. A set of subroutines for detecting the block-tridiagonal structure of a Hamiltonian matrix and splitting it into series of diagonal and off-diagonal blocks is based on a new greedy algorithm with recursion. Additionally the developed software is equipped with a set of programs for computing complex band structure, self-energies of elastic scattering processes, and Green's functions. Examples of usage and capabilities of the computational framework are illustrated by computing the band structure and transport properties of a silicon nanowire as well as the band structure of bulk bismuth.