Create TBPLaS_vote.md

A vote for TBPLaS.

Author: RussellHu41

votes/TBPLaS_vote.md

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+# A Vote for New Projects:  TBPLaS
+
+## Proposal
+
+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
+
+## Deadline
+
+The vote will be open for at least 6 days unless there is an objection.
+
+## Scope
+
+TOC MEMBERS.