TBPLaS (Tight-Binding Package for Large-scale Simulation) is a package for building and solving tight-binding models, with emphasis on handling large systems. TBPLaS implements exact diagonalization-based methods, the tight-binding propagation method (TBPM), kernel polynomial method (KPM), and Green's function method. Sparse matrices, Cython/FORTRAN extensions and hybrid OpenMP+MPI parallelization are utilized for optimal performance on modern computers. The main features of TBPLaS include:
- Capabilities
- Modeling
- Models with arbitrary dimesion, shape and boundary conditions
- Clusters, nano-tubes, slabs and crystals
- Defects, impurities and disorders
- Hetero-structures, quasicrystal, fractals
- Built-in support for Slater-Koster formulation and spin-orbital coupling
- Shipped with materials database (Graphene, phosphorene, antimonene, TMDC)
- Interfaces to Wannier90 and LAMMPS
- Tools for fitting on-site energies and hopping integrals
- Support for analytical Hamiltonian
- Fields and strains
- Homogeneous magnetic field via Peierls substitution
- User-defined electric field
- Arbitary deformation with strain and/or stress
- Exact-diagonalization
- Band structure, density of states (DOS), wave functions, topological invariants, spin textures
- Polarizability, dielectric function, optical (AC) conductivity
- Tight-binding propagation method (TBPM)
- DOS, LDOS and carrier density
- Optical (AC) conductivity and absorption spectrum
- Electronic (DC) conductivity and time-dependent diffusion coefficient
- Carrier velocity, mobility, elastic mean free path, Anderson localization length
- Polarization function, response function, dielectric function, energy loss function
- Plasmon dispersion, plasmon lifetime and damping rate
- Quasi-eigenstate and real-space charge density
- Propagation of time-dependent wave function
- Kernel polynomial method
- Electronic (DC) and Hall Conductivity
- Recursive Green's function method
- Local density of states (LDOS)
- Efficiency
- Cython (C-Extensions for Python) and FORTRAN for performance-critical parts
- Hybrid parallelism based on MPI and OpenMP
- Sparse matrices for reducing memory cost
- Lazy-evaluation techniques to reduce unnecessary operations
- Interfaced to Intel MKL (Math Kernel Library)
- User friendliness
- Intuitive object-oriented user APIs (Application Programming Interface) in Python with type hints
- Simple workflow with a lot of handy tools
- Transparent code architecture with detailed documentation
- Security
- Detailed checking procedures on input arguments
- Carefully designed exception handling with precise error message
- Top-down and bottom-up (observer pattern) techniques for keeping data consistency
See INSTALL.rst for the installation guides.
Some examples demonstrating the features of TBPLaS can be found under examples directory. More detailed tutorials can be found in the online documentation.
The documentation is available online at http://www.tbplas.net.
See CITING.rst for more details.
TBPLaS is released under the BSD license. See LICENSE.rst for more details.