{"id":13458646,"url":"https://github.com/deepmodeling/tbplas","last_synced_at":"2026-01-06T12:55:19.050Z","repository":{"id":227219810,"uuid":"764971181","full_name":"deepmodeling/tbplas","owner":"deepmodeling","description":"Repository of  TBPLaS (tight-binding package for large-scale simulation)","archived":false,"fork":false,"pushed_at":"2024-04-11T02:16:51.000Z","size":24883,"stargazers_count":9,"open_issues_count":5,"forks_count":5,"subscribers_count":9,"default_branch":"master","last_synced_at":"2025-02-01T20:16:54.799Z","etag":null,"topics":["condensed-matter-physics","solid-state-physics","tight-binding"],"latest_commit_sha":null,"homepage":"http://www.tbplas.net","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/deepmodeling.png","metadata":{"files":{"readme":"README.rst","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.rst","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-02-29T03:21:24.000Z","updated_at":"2024-12-12T15:59:30.000Z","dependencies_parsed_at":"2024-07-31T09:19:33.413Z","dependency_job_id":null,"html_url":"https://github.com/deepmodeling/tbplas","commit_stats":null,"previous_names":["deepmodeling/tbplas"],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deepmodeling%2Ftbplas","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deepmodeling%2Ftbplas/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deepmodeling%2Ftbplas/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deepmodeling%2Ftbplas/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/deepmodeling","download_url":"https://codeload.github.com/deepmodeling/tbplas/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245886375,"owners_count":20688654,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["condensed-matter-physics","solid-state-physics","tight-binding"],"created_at":"2024-07-31T09:00:54.857Z","updated_at":"2026-01-06T12:55:18.999Z","avatar_url":"https://github.com/deepmodeling.png","language":null,"readme":"Introduction\n============\n\nTBPLaS (Tight-Binding Package for Large-scale Simulation) is a package for building and solving\ntight-binding models, with emphasis on handling large systems. TBPLaS implements exact\ndiagonalization-based methods, the tight-binding propagation method (TBPM), kernel polynomial\nmethod (KPM), and Green's function method. Sparse matrices, Cython/FORTRAN extensions and hybrid\nOpenMP+MPI parallelization are utilized for optimal performance on modern computers. The main\nfeatures of TBPLaS include:\n\n* Capabilities\n    * Modeling\n        * Models with arbitrary dimesion, shape and boundary conditions\n        * Clusters, nano-tubes, slabs and crystals\n        * Defects, impurities and disorders\n        * Hetero-structures, quasicrystal, fractals\n        * Built-in support for Slater-Koster formulation and spin-orbital coupling\n        * Shipped with materials database (Graphene, phosphorene, antimonene, TMDC)\n        * Interfaces to Wannier90 and LAMMPS\n        * Tools for fitting on-site energies and hopping integrals\n        * Support for analytical Hamiltonian\n    * Fields and strains\n        * Homogeneous magnetic field via Peierls substitution\n        * User-defined electric field\n        * Arbitary deformation with strain and/or stress\n    * Exact-diagonalization\n        * Band structure, density of states (DOS), wave functions, topological invariants, spin textures\n        * Polarizability, dielectric function, optical (AC) conductivity\n    * Tight-binding propagation method (TBPM)\n        * DOS, LDOS and carrier density\n        * Optical (AC) conductivity and absorption spectrum\n        * Electronic (DC) conductivity and time-dependent diffusion coefficient\n        * Carrier velocity, mobility, elastic mean free path, Anderson localization length \n        * Polarization function, response function, dielectric function, energy loss function\n        * Plasmon dispersion, plasmon lifetime and damping rate\n        * Quasi-eigenstate and real-space charge density\n        * Propagation of time-dependent wave function\n    * Kernel polynomial method\n        * Electronic (DC) and Hall Conductivity  \n    * Recursive Green's function method\n        * Local density of states (LDOS)\n* Efficiency\n    * Cython (C-Extensions for Python) and FORTRAN for performance-critical parts\n    * Hybrid parallelism based on MPI and OpenMP\n    * Sparse matrices for reducing memory cost\n    * Lazy-evaluation techniques to reduce unnecessary operations\n    * Interfaced to Intel MKL (Math Kernel Library)\n* User friendliness\n    * Intuitive object-oriented user APIs (Application Programming Interface) in Python with type hints\n    * Simple workflow with a lot of handy tools\n    * Transparent code architecture with detailed documentation\n* Security\n    * Detailed checking procedures on input arguments\n    * Carefully designed exception handling with precise error message\n    * Top-down and bottom-up (observer pattern) techniques for keeping data consistency\n\nInstallation\n------------\n\nSee *INSTALL.rst* for the installation guides.\n\nTutorials\n---------\n\nSome examples demonstrating the features of TBPLaS can be found under *examples* directory.\nMore detailed tutorials can be found in the online documentation.\n\nDocumentation\n-------------\n\nThe documentation is available online at `\u003chttp://www.tbplas.net\u003e`_.\n\nCitation\n--------\n\nSee *CITING.rst* for more details.\n\nLicense\n-------\n\nTBPLaS is released under the BSD license. See *LICENSE.rst* for more details.\n","funding_links":[],"categories":["Others"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeepmodeling%2Ftbplas","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdeepmodeling%2Ftbplas","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeepmodeling%2Ftbplas/lists"}