maintainers:	lukasturcani, andrewtarzia
documentation:	https://stk.readthedocs.io
discord:	https://discord.gg/zbCUzuxe2B

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:

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:

pip install stk --upgrade

Developer Setup

1. Install just.
2. In a new virtual environment run:

just dev

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

just mongo

4. Run code checks:

just check

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

• Describing metal-organic cage usage: Unlocking the computational design of metal–organic cages

• (Out of date) stk: A Python Toolkit for Supramolecular Assembly | chemrxiv

using stk

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

• The effect of disorder in multi-component covalent organic frameworks

• Tetramine Aspect Ratio and Flexibility Determine Framework Symmetry for Zn8L6 Self-Assembled Structures

• Orientational self-sorting in cuboctahedral Pd cages

• Conformer-RL: A deep reinforcement learning library for conformer generation

• High-throughput Computational Evaluation of Low Symmetry Pd2L4 Cages to Aid in System Design

• Forecasting System of Computational Time of DFT/TDDFT Calculations under the Multiverse Ansatz via Machine Learning and Cheminformatics

• 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

• Accelerated Discovery of Organic Polymer Photocatalysts for Hydrogen Evolution from Water through the Integration of Experiment and Theory

• Structurally Diverse Covalent Triazine-Based Framework Materials for Photocatalytic Hydrogen Evolution from Water

• Mapping Binary Copolymer Property Space with Neural Networks

• An Evolutionary Algorithm for the Discovery of Porous Organic Cages | chemrxiv

• Machine Learning for Organic Cage Property Prediction | chemrxiv

• A High-Throughput Screening Approach for the Optoelectronic Properties of Conjugated Polymers | chemrxiv

• Computationally-Inspired Discovery of an Unsymmetrical Porous Organic Cage | chemrxiv

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

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.