Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/lukasturcani/stk

A Python library which allows construction and manipulation of complex molecules, as well as automatic molecular design and the creation of molecular databases.
https://github.com/lukasturcani/stk

cheminformatics chemistry computational-chemistry computational-science materials-design materials-discoveries materials-science materials-screening molecular-evolution molecular-modeling molecular-structures reactions

Last synced: 14 days ago
JSON representation

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

Awesome Lists containing this project

README 

        
:maintainers:

  `lukasturcani `_,

  `andrewtarzia `_

:documentation: https://stk.readthedocs.io

:discord: https://discord.gg/zbCUzuxe2B



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



.. image:: https://github.com/lukasturcani/stk/actions/workflows/tests.yml/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. The documentation of ``stk`` is available on

https://stk.readthedocs.io and the project's Discord server can be

joined through https://discord.gg/zbCUzuxe2B.



Installation

============



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



.. code-block:: bash



  pip install stk



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:



.. code-block:: bash



  pip install stk --upgrade



Developer Setup

---------------



1. Install `just`_.

2. In a new virtual environment run:



.. code-block:: bash



  just dev



3. Setup the `MongoDB`_ container (make sure ``docker`` is installed):



.. code-block:: bash



  just mongo



4. Run code checks:



.. code-block:: bash



  just check



.. _`just`: https://github.com/casey/just

.. _`MongoDB`: https://www.mongodb.com/docs/manual/tutorial/install-mongodb-on-ubuntu/



How To Cite

===========



If you use ``stk`` please cite



  https://github.com/lukasturcani/stk



and



  https://aip.scitation.org/doi/10.1063/5.0049708



Publications

============



about stk

---------



* `stk: An Extendable Python Framework for Automated Molecular and

  Supramolecular Structure Assembly and Discovery`__



__ https://aip.scitation.org/doi/10.1063/5.0049708



* Describing metal-organic cage usage: `Unlocking the computational design of metal–organic cages`__



__ https://pubs.rsc.org/en/content/articlelanding/2022/CC/D2CC00532H



* (Out of date) `stk: A Python Toolkit for Supramolecular Assembly`__

  | chemrxiv__



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

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



using stk

---------



* Using stk for constructing larger numbers of coarse-grained models: `Systematic exploration of accessible topologies of cage molecules via minimalistic models`__



__ https://pubs.rsc.org/en/content/articlelanding/2023/sc/d3sc03991a



* `The effect of disorder in multi-component covalent organic frameworks`__



__ https://pubs.rsc.org/en/content/articlehtml/2023/cc/d3cc01111a



* `Tetramine Aspect Ratio and Flexibility Determine Framework Symmetry for Zn8L6 Self-Assembled Structures`__



__ https://onlinelibrary.wiley.com/doi/10.1002/anie.202217987



* `Orientational self-sorting in cuboctahedral Pd cages`__



__ https://pubs.rsc.org/en/content/articlehtml/2022/sc/d2sc03856k



* `Conformer-RL: A deep reinforcement learning library for conformer

  generation`__



__ https://onlinelibrary.wiley.com/doi/full/10.1002/jcc.26984



* `High-throughput Computational Evaluation of Low Symmetry Pd2L4

  Cages to Aid in System Design`__



__ https://onlinelibrary.wiley.com/doi/10.1002/anie.202106721



* `Forecasting System of Computational Time of DFT/TDDFT Calculations

  under the Multiverse Ansatz via Machine Learning and

  Cheminformatics`__



__ https://pubs.acs.org/doi/full/10.1021/acsomega.0c04981



* `Using High-throughput Virtual Screening to Explore the

  Optoelectronic Property Space of Organic Dyes; Finding

  Diketopyrrolopyrrole Dyes for Dye-sensitized Water Splitting and

  Solar Cells`__



__ https://pubs.rsc.org/en/content/articlelanding/2021/SE/D0SE00985G#!divAbstract



* `Accelerated Discovery of Organic Polymer Photocatalysts for Hydrogen

  Evolution from Water through the Integration of Experiment and

  Theory`__



__ https://pubs.acs.org/doi/abs/10.1021/jacs.9b03591



* `Structurally Diverse Covalent Triazine-Based Framework Materials for

  Photocatalytic Hydrogen Evolution from Water`__



__ https://pubs.acs.org/doi/full/10.1021/acs.chemmater.9b02825



* `Mapping Binary Copolymer Property Space with Neural Networks`__



__ https://pubs.rsc.org/ko/content/articlehtml/2019/sc/c8sc05710a



* `An Evolutionary Algorithm for the Discovery of Porous Organic

  Cages`__ | chemrxiv__



__ https://pubs.rsc.org/en/content/articlelanding/2018/sc/c8sc03560a#!divAbstract

__ https://chemrxiv.org/articles/An_Evolutionary_Algorithm_for_the_Discovery_of_Porous_Organic_Cages/6954557



* `Machine Learning for Organic Cage Property Prediction`__

  | chemrxiv__



__ https://pubs.acs.org/doi/10.1021/acs.chemmater.8b03572

__ https://chemrxiv.org/articles/Machine_Learning_for_Organic_Cage_Property_Prediction/6995018



* `A High-Throughput Screening Approach for the Optoelectronic

  Properties of Conjugated Polymers`__ | chemrxiv__



__ https://pubs.acs.org/doi/abs/10.1021/acs.jcim.8b00256

__ https://chemrxiv.org/articles/A_High-Throughput_Screening_Approach_for_the_Optoelectronic_Properties_of_Conjugated_Polymers/6181841



* `Computationally-Inspired Discovery of an Unsymmetrical Porous

  Organic Cage`__ | chemrxiv__



__ https://pubs.rsc.org/en/content/articlelanding/2018/nr/c8nr06868b#!divAbstract

__ https://chemrxiv.org/articles/Computationally-Inspired_Discovery_of_an_Unsymmetrical_Porous_Organic_Cage/6863684



* `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.