TBPLaS: a Tight-Binding Package for Large-scale Simulation

TL;DR

TBPLaS is an open-source, efficient Python-based software for large-scale tight-binding simulations, enabling accurate electronic and optical property calculations of complex systems with billions of orbitals.

Contribution

It introduces a flexible, high-performance tight-binding package utilizing TBPM for linear-scaling large system simulations with an intuitive API.

Findings

Supports large-scale simulations with billions of orbitals

Provides accurate calculations of electronic and optical properties

Offers an easy-to-use, extensible framework

Abstract

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 Schr\"odinger 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.