NanoNET swMATH ID: 35709 Software Authors: M. V. Klymenko, J. A. Vaitkus, J. S. Smith, J. H. Cole Description: NanoNET: an extendable Python framework for semi-empirical tight-binding models. 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. Homepage: https://arxiv.org/abs/2010.07463 Source Code: https://github.com/freude/NanoNet Dependencies: Python Keywords: Python framework; Computational Physics; arXiv_physics.comp-ph; Nanoscale; Electron Transport; tight-binding method; Hamiltonian matrix; kd-tree; band matrix; block-tridiagonal matrix; non-equilibrium; Green functions Related Software: Kwant; PythTB; Z2pack; ISOTROPY; SpaceGroupIrep; WannierTools; Qsymm; ABINIT; Quantum Espresso; Wannier90; AFLOW; Mathematica; MagneticTB; ASE; SMEAGOL; pybinding; GOLLUM; GPAW; TensorFlow; LAPACK Cited in: 0 Publications Standard Articles 1 Publication describing the Software Year NanoNET: an extendable Python framework for semi-empirical tight-binding models M. V. Klymenko, J. A. Vaitkus, J. S. Smith, J. H. Cole 2020