.. DeePTB documentation master file.
   You can adapt this file completely to your liking, but it should at least
   contain the root `toctree` directive.

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DPNEGF Documentation
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**DPNEGF** is a Python package that integrates the Deep Learning Tight-Binding (**DeePTB**) approach with the Non-Equilibrium Green's Function (**NEGF**) method, 
establishing an efficient quantum transport simulation framework **DeePTB-NEGF** with first-principles accuracy. 

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Overview
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Key Features:
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By using DeePTB-SK or DeePTB-E3—both available within the DeePTB package—DeePTB-NEGF can compute quantum transport 
properties in open-boundary systems with either environment-corrected Slater-Koster(SK) TB Hamiltonian 
or linear combination of atomic orbitals (LCAO) Kohn-Sham Hamiltonian.


For more details, see our papers:

* `DeePTB-NEGF: arXiv:2411.08800 <https://arxiv.org/abs/2411.08800v2>`_
* `DeePTB-SK: Nat Commun 15, 6772 (2024) <https://doi.org/10.1038/s41467-024-51006-4>`_
* `DeePTB-E3: ICLR 2025 Spotlight <https://openreview.net/forum?id=kpq3IIjUD3>`_


.. toctree::
   :maxdepth: 2
   :caption: Quick Start

   easy_install
   hands_on/index

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..    :maxdepth: 2
..    :caption: INPUT TAG
   
..    input_params/index

.. toctree::
   :maxdepth: 2
   :caption: Citing DeePTB-NEGF

   CITATIONS

.. toctree::
   :maxdepth: 2
   :caption: Developing Team

   DevelopingTeam

.. toctree::
   :maxdepth: 2
   :caption: Community

   CONTRIBUTING