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/ PythTB Public archive

Original release history of the celebrated tight-binding python package developed by Sinisa Coh and David Vanderbilt. Now archived as an official repository has appeared on GitHub. See link below:

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PythTB 2.0.0 has been finally announced! It brings a most welcome, wide,
reorganization of the whole codebase, not anymore monolithic, but tidy,
modular and much more maintainable. In the same spirit, they are now also
publicly sharing the git reporitory on GitHub (https://github.com/pythtb),
making this fork not needed anymore. I am happy to archive it now, as a
log of all the git-versioned past releases (the new official repository
goes back to 2018, only).

You can find the official release notes for version 2.0.0 at
https://pythtb.readthedocs.io/en/latest/release/2.0.0-notes.html

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PythTB is a software package providing a Python implementation of the
tight-binding approximation. It can be used to construct and solve
tight-binding models of the electronic structure of systems of
arbitrary dimensionality (crystals, slabs, ribbons, clusters, etc.),
and is rich with features for computing Berry phases and related
properties.

Here we provide a fork of the project, with all the released development
history, as found in the primary location for the package:

   http://www.physics.rutgers.edu/pythtb/

where the most up-to-date docs can be found. For now this fork is virtually
untouched, but development of new custom features might happen in the future.
For the pure original development history (and future official releases)
please look at the /rutgers branch.

Another interesting fork here on github is the numba-accelerated version
by Mikel GarcΓ­a and IΓ±igo Robredo: https://github.com/mikelgda/yeet-pythtb
> Note that PythTB 2.0.0 is probably comparable in performance, or even faster
> if installed with the Tensorflow optional bindings 😁

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Original release history of the celebrated tight-binding python package developed by Sinisa Coh and David Vanderbilt. Now archived as an official repository has appeared on GitHub. See link below:

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