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stk is a Python library which allows construction andmanipulation of complex molecules, as well as automaticmolecular design, and the creation of molecular, and molecularproperty, databases. The documentation ofstk is available onhttps://stk.readthedocs.io and the project's Discord server can bejoined throughhttps://discord.gg/zbCUzuxe2B.

To getstk, you can install it with pip:

pip install stk

If you would like to get updated when a new release ofstk comesout, which happens pretty regularly, click on thewatch button onthe top right corner of the GitHub page. Then selectReleases onlyfrom the dropdown menu.

You can see the latest releases here:

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

There will be a corresponding release onpip for each releaseon GitHub, and you can update yourstk with:

pip install stk --upgrade

1. Installjust.
2. In a new virtual environment run:

just dev

3. Setup theMongoDB container (make suredocker is installed):

just mongo

4. Run code checks:

just check

If you usestk please cite

https://github.com/lukasturcani/stk

and

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

• stk: An Extendable Python Framework for Automated Molecular andSupramolecular 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• (Out of date)stk: A Python Toolkit for Supramolecular Assembly|chemrxiv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 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 conformergeneration

• High-throughput Computational Evaluation of Low Symmetry Pd2L4Cages to Aid in System Design

• Forecasting System of Computational Time of DFT/TDDFT Calculationsunder the Multiverse Ansatz via Machine Learning andCheminformatics

• Using High-throughput Virtual Screening to Explore theOptoelectronic Property Space of Organic Dyes; FindingDiketopyrrolopyrrole Dyes for Dye-sensitized Water Splitting andSolar Cells

• Accelerated Discovery of Organic Polymer Photocatalysts for HydrogenEvolution from Water through the Integration of Experiment andTheory

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

• Mapping Binary Copolymer Property Space with Neural Networks

• An Evolutionary Algorithm for the Discovery of Porous OrganicCages |chemrxiv

• Machine Learning for Organic Cage Property Prediction|chemrxiv

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

• Computationally-Inspired Discovery of an Unsymmetrical PorousOrganic Cage |chemrxiv

• Maximising the Hydrogen Evolution Activity in Organic Photocatalystsby co-Polymerisation

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