:author: Lukas Turcani :Documentation: https://stk.readthedocs.io

.. figure:: docs/source/figures/stk.png

.. image:: https://github.com/lukasturcani/stk/workflows/tests/badge.svg?branch=master :target: https://github.com/lukasturcani/stk/actions?query=branch%3Amaster

.. image:: https://readthedocs.org/projects/stk/badge/?version=latest :target: https://stk.readthedocs.io

Overview

stk is a Python library which allows construction and manipulation of complex molecules, as well as automatic molecular design, and the creation of molecular, and molecular property, databases.

Installation

To get stk, you can install it with pip::

$ pip install stk

Make sure you also install rdkit, which is a dependency::

$ conda install -c rdkit rdkit=2020

If you would like to get updated when a new release of stk comes out, which happens pretty regularly, click on the watch button on the top right corner of the GitHub page. Then select Releases only from the dropdown menu.

You can see the latest releases here:

https://github.com/lukasturcani/stk/releases

There will be a corresponding release on pip for each release on GitHub, and you can update your stk with::

$ pip install stk --upgrade

How To Cite

If you use stk please cite

https://github.com/lukasturcani/stk

and

https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.25377

Publications

about stk

• stk: A Python Toolkit for Supramolecular Assembly_ | chemrxiv__

__ https://chemrxiv.org/articles/STK_A_Python_Toolkit_for_Supramolecular_Assembly/6127826

.. _stk: A Python Toolkit for Supramolecular Assembly: https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.25377

using stk

• An Evolutionary Algorithm for the Discovery of Porous Organic Cages_ | chemrxiv__

__ https://chemrxiv.org/articles/An_Evolutionary_Algorithm_for_the_Discovery_of_Porous_Organic_Cages/6954557 .. _An Evolutionary Algorithm for the Discovery of Porous Organic Cages: https://pubs.rsc.org/en/content/articlelanding/2018/sc/c8sc03560a#!divAbstract

• Machine Learning for Organic Cage Property Prediction_ | chemrxiv__

__ https://chemrxiv.org/articles/Machine_Learning_for_Organic_Cage_Property_Prediction/6995018 .. _Machine Learning for Organic Cage Property Prediction: https://pubs.acs.org/doi/10.1021/acs.chemmater.8b03572

• A High-Throughput Screening Approach for the Optoelectronic Properties of Conjugated Polymers_ | chemrxiv__

__ https://chemrxiv.org/articles/A_High-Throughput_Screening_Approach_for_the_Optoelectronic_Properties_of_Conjugated_Polymers/6181841 .. _A High-Throughput Screening Approach for the Optoelectronic Properties of Conjugated Polymers: https://pubs.acs.org/doi/abs/10.1021/acs.jcim.8b00256

• Computationally-Inspired Discovery of an Unsymmetrical Porous Organic Cage_ | chemrxiv__

__ https://chemrxiv.org/articles/Computationally-Inspired_Discovery_of_an_Unsymmetrical_Porous_Organic_Cage/6863684 .. _Computationally-Inspired Discovery of an Unsymmetrical Porous Organic Cage: https://pubs.rsc.org/en/content/articlelanding/2018/nr/c8nr06868b#!divAbstract

• Maximising the Hydrogen Evolution Activity in Organic Photocatalysts by co-Polymerisation_

.. _Maximising the Hydrogen Evolution Activity in Organic Photocatalysts by co-Polymerisation: https://pubs.rsc.org/en/Content/ArticleLanding/TA/2018/C8TA04186E#!divAbstract

Acknowledgements

I began developing this code when I was working in the Jelfs group, http://www.jelfs-group.org/, whose members often provide me with very valuable feedback, which I gratefully acknowledge.