stk: A python toolkit for supramolecular assembly.

Area of Science:

• Materials Science
• Computational Chemistry
• Supramolecular Chemistry

Background:

• Designing and optimizing supramolecular materials is complex and time-consuming.
• Existing computational tools may lack modularity or extensibility for diverse supramolecular structures.

Purpose of the Study:

• To present a novel, open-source Python library for the automated assembly, molecular optimization, and property calculation of supramolecular materials.
• To provide a flexible and extensible platform for researchers in supramolecular chemistry and materials science.

Main Methods:

• Development of a modular and extensible Python library (stk) with a simple API.
• Integration with third-party computational codes for energy landscape exploration and property calculation.
• Support for constructing various supramolecular architectures including polymers, cages, and covalent organic frameworks (COFs).

Main Results:

• stk facilitates automated construction and analysis of diverse supramolecular materials.
• The library supports high-throughput screening of large material datasets.
• Designed for straightforward extension to new supramolecular types (e.g., MOFs, catenanes) and topologies.

Conclusions:

• stk offers a powerful and versatile tool for accelerating the discovery and design of novel supramolecular materials.
• The open-source nature and modular design promote collaboration and further development in the field.