Abstract We present a novel open-source Python framework called NanoNET (Nanoscale Non-equilibrium Electron Transport) for modeling 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 k d -tree nearest-neighbor 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. Program summary Program Title: NanoNET CPC Library link to program files: http://dx.doi.org/10.17632/b9p7kyzdj9.1 Developer’s repository link: http://github.com/freude/NanoNet Licensing provisions: MIT Programming language: Python Nature of problem: The framework NanoNET solves a problem which is, having a set of atomic coordinates and tight-binding parameters, to construct Hamiltonian matrices in one of several desired forms. In particular, some applications require those matrices to have a reduced bandwidth and/or to possess a block-tridiagonal structure. Solution method: The problem is solved using a combination of k d -tree-based fast nearest-neighbor search and atomic coordinate sorting. Furthermore, a new greedy recursive algorithm is proposed for detecting block-tridiagonal structure of a matrix in a non-optimal way. Additionally, we propose an algorithm of a polynomial time for optimizing block sizes. Additional features: Although the resulting matrices can be processed by many existing software packages, the framework also has built-in standard tools for diagonalizing Hamiltonian matrices and computing Green’s functions that make it an independent tool for solving electronic structure and transport problems.

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NanoNET：用于半经验紧束缚模型的可扩展 Python 框架

摘要 我们提出了一种名为 NanoNET（纳米级非平衡电子传输）的新型开源 Python 框架，用于对电子结构和传输进行建模。我们的方法基于紧束缚方法和非平衡格林函数理论。该框架的核心功能是为从原子坐标列表和密集、稀疏或块三对角形式的双中心积分的查找表中有效构建紧束缚哈密顿矩阵提供便利。该框架实现了一种基于 kd -tree 最近邻搜索的方法，适用于孤立的原子簇和周期结构。一组用于检测哈密顿矩阵的块三对角结构并将其拆分为一系列对角和非对角块的子程序基于具有递归的新贪心算法。此外，开发的软件配备了一套用于计算复杂能带结构、弹性散射过程的自能和格林函数的程序。通过计算硅纳米线的能带结构和传输特性以及体铋的能带结构来说明计算框架的使用和能力的示例。程序摘要 程序名称：NanoNET CPC 库程序文件链接：http://dx.doi.org/10.17632/b9p7kyzdj9.1 开发者存储库链接：http://github.com/freude/NanoNet 许可条款：MIT 编程语言： Python 问题性质：NanoNET 框架解决了一个问题，即具有一组原子坐标和紧束缚参数，以几种所需形式之一构造哈密顿矩阵。特别是，一些应用要求那些矩阵具有减小的带宽和/或具有块三对角结构。解决方法：结合基于kd-tree的快速最近邻搜索和原子坐标排序来解决问题。此外，提出了一种新的贪婪递归算法，用于以非最优方式检测矩阵的块三对角结构。此外，我们提出了一种优化块大小的多项式时间算法。附加功能：虽然生成的矩阵可以被许多现有的软件包处理，