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Merge pull request #36 from RussellHu41/patch-4
Create TBPLaS_vote.md
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votes/TBPLaS_vote.md

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# A Vote for New Projects: TBPLaS
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## Proposal
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TBPLaS (Tight-Binding Package for Large-scale Simulation) is a package for building and solving
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tight-binding models, with emphasis on handling large systems. TBPLaS implements exact
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diagonalization-based methods, the tight-binding propagation method (TBPM), kernel polynomial
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method (KPM), and Green's function method. Sparse matrices, Cython/FORTRAN extensions and hybrid
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OpenMP+MPI parallelization are utilized for optimal performance on modern computers. The main
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features of TBPLaS include:
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* Capabilities
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* Modeling
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* Models with arbitrary dimesion, shape and boundary conditions
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* Clusters, nano-tubes, slabs and crystals
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* Defects, impurities and disorders
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* Hetero-structures, quasicrystal, fractals
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* Built-in support for Slater-Koster formulation and spin-orbital coupling
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* Shipped with materials database (Graphene, phosphorene, antimonene, TMDC)
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* Interfaces to Wannier90 and LAMMPS
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* Tools for fitting on-site energies and hopping integrals
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* Support for analytical Hamiltonian
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* Fields and strains
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* Homogeneous magnetic field via Peierls substitution
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* User-defined electric field
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* Arbitary deformation with strain and/or stress
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* Exact-diagonalization
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* Band structure, density of states (DOS), wave functions, topological invariants, spin textures
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* Polarizability, dielectric function, optical (AC) conductivity
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* Tight-binding propagation method (TBPM)
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* DOS, LDOS and carrier density
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* Optical (AC) conductivity and absorption spectrum
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* Electronic (DC) conductivity and time-dependent diffusion coefficient
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* Carrier velocity, mobility, elastic mean free path, Anderson localization length
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* Polarization function, response function, dielectric function, energy loss function
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* Plasmon dispersion, plasmon lifetime and damping rate
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* Quasi-eigenstate and real-space charge density
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* Propagation of time-dependent wave function
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* Kernel polynomial method
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* Electronic (DC) and Hall Conductivity
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* Recursive Green's function method
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* Local density of states (LDOS)
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* Efficiency
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* Cython (C-Extensions for Python) and FORTRAN for performance-critical parts
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* Hybrid parallelism based on MPI and OpenMP
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* Sparse matrices for reducing memory cost
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* Lazy-evaluation techniques to reduce unnecessary operations
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* Interfaced to Intel MKL (Math Kernel Library)
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* User friendliness
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* Intuitive object-oriented user APIs (Application Programming Interface) in Python with type hints
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* Simple workflow with a lot of handy tools
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* Transparent code architecture with detailed documentation
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* Security
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* Detailed checking procedures on input arguments
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* Carefully designed exception handling with precise error message
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* Top-down and bottom-up (observer pattern) techniques for keeping data consistency
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## Deadline
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The vote will be open for at least 6 days unless there is an objection.
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## Scope
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TOC MEMBERS.
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## Result:
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Approved by:
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Kuang Yu
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Jameswind

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