Skip to main content

tb3: an open-source software package for accurate and efficient electronic structure calculations using tight-binding (TB), including three-body interactions. https://pages.nist.gov/ThreeBodyTB.jl

Project description

![alt text](https://github.com/usnistgov/tb3py/actions/workflows/main.yml/badge.svg) # TB3PY

by Kevin F. Garrity and Kamal Choudhary

This is a python wrapper for the [ThreeBodyTB.jl](http://github.com/usnistgov/ThreeBodyTB.jl) Julia package, which runs two- and three-body tight-binding calculations for materials.

Using the Julia version directly is the primary method for running the code. However, this code is created to help users who might be more familiar with python using the [PyJulia](https://github.com/JuliaPy/pyjulia) interface.

## Installation

First create a conda environment: Install miniconda environment from https://conda.io/miniconda.html Based on your system requirements, you’ll get a file something like ‘Miniconda3-latest-XYZ’.

Now,

` bash Miniconda3-latest-Linux-x86_64.sh (for linux) bash Miniconda3-latest-MacOSX-x86_64.sh (for Mac) ` Download 32/64 bit python 3.6 miniconda exe and install (for windows) Now, let’s make a conda environment, say “version”, choose other name as you like:: ` conda create --name my_tb3 python=3.8 source activate my_tb3 `

Now, let’s install the package: ` git clone https://github.com/usnistgov/tb3py.git cd tb3py python setup.py develop `

Note that this can take a while and may use significant disk space. The code will, if necessary, a) download & install Julia b) download & install ThreeBodyTB.jl, and c) create a system image for fast loading.

To check if the installation went well, please try:

` pytest tb3py/tests/test_ptop.py `

For main documentation of ThreeBodyTB.jl, see [![](https://img.shields.io/badge/docs-dev-blue.svg)](https://pages.nist.gov/ThreeBodyTB.jl/) This code is only the wrapper that downloads and installs that code.

## Examples

  • Predict total energy, electronic bandgap and bandstructure for a system using POSCAR file:

    ` python tb3py/main.py --poscar_file tb3py/examples/POSCAR `

  • Predict total energy, electronic bandgap and bandstructure for a system using cif file:

    ` python tb3py/main.py --cif_file tb3py/examples/JVASP-1002.cif `

## Performance Tips

  • Julia can take advantage multiple threads. Try setting the environment variable below as appropriate for your machine.
    ` JULIA_NUM_THREADS=8 export JULIA_NUM_THREADS `
  • Note that despite using pre-compilation where possible, some functions will run faster the second time you run them due to the jit.
  • Note that you must delete the system image if you want to update the ThreeBodyTB.jl code and re-run the installation.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

tb3py-2021.7.10.tar.gz (6.9 kB view hashes)

Uploaded source

Built Distribution

tb3py-2021.7.10-py2.py3-none-any.whl (6.5 kB view hashes)

Uploaded py2 py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page