Metadata-Version: 2.1
Name: tb-mean-field-hubbard
Version: 1.2.0
Summary: Package to run tight-binding mean field hubbard calculations
Home-page: https://github.com/eimrek/tb-mean-field-hubbard
Author: Kristjan Eimre
Author-email: kristjaneimre@gmail.com
License: UNKNOWN
Description: [![DOI](https://zenodo.org/badge/314531046.svg)](https://zenodo.org/badge/latestdoi/314531046)
        
        # tb-mean-field-hubbard 
        
        Python library to perform tight-binding mean field Hubbard calculations on the conjugated π-networks of organic systems.
        Only carbon atoms are supported and each atom is modelled by a single p<sub>z</sub> orbital hosting a single electron.
        
        The modelled Hamiltonian is the following:
        
        ![](https://latex.codecogs.com/svg.latex?\dpi{280}\large{\hat{H}_\text{MFH}=-t\sum\limits_{\langle{i,j}\rangle,\sigma}\left(\hat{c}^{\dag}_{i,\sigma}\hat{c}_{j,\sigma}+\text{h.c.}\right)+U\sum\limits_{i,\sigma}\langle{\hat{n}_{i,\sigma}}\rangle%20\hat{n}_{i,\overline{\sigma}}-U\sum\limits_{i}\langle{\hat{n}_{i,\uparrow}}\rangle\langle{\hat{n}_{i,\downarrow}}\rangle,})
        
        where c<sup>†</sup>, c and n are respectively the creation, annihiliation and number operators, t is the hopping integral and U denotes the on-site Coulomb repulsion.
        
        ### System requirements
        
        Python 3 (tested with 3.6 - 3.9) environment is required with the following libraries (parenthesis indicate tested versions; but the library should work with all recent versions):
        * Standard python libraries: `numpy` (1.17.2), `scipy` (1.3.1), `matplotlib` (3.2.1)
        * Atomistic simulation environment: `ase` (3.18.1)
        * Python Tight Binding: `pythtb` (1.7.2)
        
        ### Installation
        
        Option 1) To install the dependencies and the library, and to have access to the code and the notebook
        ```
        git clone https://github.com/eimrek/tb-mean-field-hubbard.git
        cd tb-mean-field-hubbard
        pip install -e .
        ```
        
        Option 2) To just install the dependencies and the library
        ```
        pip install git+https://github.com/eimrek/tb-mean-field-hubbard.git#egg=tb-mean-field-hubbard
        ```
        
        Option 3) If dependencies are already installed, then simply downloading the code and executing the notebook will work.
        
        In all cases, on a normal desktop computer, installation of the python dependencies can take some minutes, while the `tb-mean-field-hubbard` should install in seconds.
        
        ### Example usage
        
        Example jupyter notebook `mfh.ipynb` is provided that performs the calculation for the Clar's goblet molecule. The geometry is read from a `xyz` file. The whole notebook should run in a matter of seconds on a normal desktop computer.
        The following image demonstrates a selection of the output for the calculation for parameters `t=2.7` and `U=3.0` (both in electronvolts).
        
        <p align="center"><img class="marginauto" src="misc/example-output.png" width="700"></p>
        
        In addition to the structure of Clar's goblet, the `geom/` folder contains input geometries for triangulene, 4- and 5-rhombene, and several other systems. 
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
