stk: An extendable Python framework for automated molecular and supramolecular structure assembly and discovery

Turcani, Lukas; Tarzia, Andrew; Szczypiński, Filip T.; Jelfs, Kim E.

Abstract

Computational software workflows are emerging as all-in-one solutions to speed up the discovery of new materials. Many computational approaches require the generation of realistic structural models for property prediction and candidate screening. However, molecular and supramolecular materials represent classes of materials with many potential applications for which there is no go-to database of existing structures or general protocol for generating structures. Here, we report a new version of the supramolecular toolkit, stk, an open-source, extendable, and modular Python framework for general structure generation of (supra)molecular structures. Our construction approach works on arbitrary building blocks and topologies and minimizes the input required from the user, making stk user-friendly and applicable to many material classes. This version of stk includes metal-containing structures and rotaxanes as well as general implementation and interface improvements. Additionally, this version includes built-in tools for exploring chemical space with an evolutionary algorithm and tools for database generation and visualization. The latest version of stk is freely available at github.com/lukasturcani/stk.

Citation

Turcani, L., Tarzia, A., Szczypiński, F. T., & Jelfs, K. E. (2021). stk: An extendable Python framework for automated molecular and supramolecular structure assembly and discovery. The Journal of Chemical Physics, 154(21), Article 214102. https://doi.org/10.1063/5.0049708

• Journal Article Type: Article
• Acceptance Date: May 12, 2021
• Publication Date: Jun 7, 2021
• Deposit Date: Feb 19, 2025
• Journal: Journal of Chemical Physics
• Print ISSN: 0021-9606
• Electronic ISSN: 1089-7690
• Publisher: American Institute of Physics
• Peer Reviewed: Peer Reviewed
• Volume: 154
• Issue: 21
• Article Number: 214102
• DOI: https://doi.org/10.1063/5.0049708
• Public URL: https://durham-repository.worktribe.com/output/3490162