论文标题
TBPLAS：用于大规模模拟的紧密结合包装
TBPLaS: a Tight-Binding Package for Large-scale Simulation
论文作者
论文摘要
TBPLAS是一个开源软件包，用于使用紧密结合（TB）理论的任意几何形状和维度进行准确模拟物理系统。它具有直观的面向对象的Python应用程序接口（API）和Cython/Fortran扩展，以确保灵活性和效率。在引擎盖下，数值计算主要由精确的对角钙化和紧密结合的传播方法（TBPM）进行，而无需对角度化。尤其是，TBPM基于时间依赖性Schrödinger方程的数值解决方案，在内存和CPU成本中以系统大小实现线性缩放。因此，TBPLA提供了一种数值便宜的方法，可以计算具有数十亿个原子轨道的大型紧密结合模型的电子，传输和光学性质。 Current capabilities of TBPLaS include the calculation of band structure, density of states, local density of states, quasi-eigenstates, optical conductivity, electrical conductivity, Hall conductivity, polarization function, dielectric function, plasmon dispersion, carrier mobility and velocity, localization length and free path, Z2 topological invariant, wave-packet propagation, etc. All the properties can be obtained with only a few lines of code.由于其可扩展和模块化的性质，可以轻松实施涉及紧密结合的哈密顿量的其他算法。在本文中，我们讨论了TBPLAS的理论框架，实施细节和共同工作流程，并提供了一些有关其应用的演示。
TBPLaS is an open-source software package for the accurate simulation of physical systems with arbitrary geometry and dimensionality utilizing the tight-binding (TB) theory. It has an intuitive object-oriented Python application interface (API) and Cython/Fortran extensions for the performance critical parts, ensuring both flexibility and efficiency. Under the hood, numerical calculations are mainly performed by both exact diagonalizatin and the tight-binding propagation method (TBPM) without diagonalization. Especially, the TBPM is based on the numerical solution of time-dependent Schrödinger equation, achieving linear scaling with system size in both memory and CPU costs. Consequently, TBPLaS provides a numerically cheap approach to calculate the electronic, transport and optical properties of large tight-binding models with billions of atomic orbitals. Current capabilities of TBPLaS include the calculation of band structure, density of states, local density of states, quasi-eigenstates, optical conductivity, electrical conductivity, Hall conductivity, polarization function, dielectric function, plasmon dispersion, carrier mobility and velocity, localization length and free path, Z2 topological invariant, wave-packet propagation, etc. All the properties can be obtained with only a few lines of code. Other algorithms involving tight-binding Hamiltonians can be implemented easily thanks to its extensible and modular nature. In this paper, we discuss the theoretical framework, implementation details and common workflow of TBPLaS, and give a few demonstrations of its applications.
