Technical Report 329, c4e-Preprint Series, Cambridge

Authors: Aleksandar Kondinski, Andrea M Oyarzún, Simon D. Rihm, Jiaru Bai, Sebastian Mosbach, Jethro Akroyd, and Markus Kraft*

Reference: Technical Report 329, c4e-Preprint Series, Cambridge, 2024

Associated Themes:
 

• Developed an automated workflow for constructing computation-ready MOPs.
• Extended assembly modeling to include polyoxometalates in MOP design.
• Generated MOP geometries closely matching experimental data.
• Workflow enables identifying MOPs for iodine capture applications.
• Structures are accessible via TWA-Marie agent with interactive 3D visuals.

Assembly modelling has been achieved in knowledge AI systems for the automated inference of new and rational metal-organic polyhedra (MOPs) (J. Am. Chem. Soc. 2022, 144, 26, 11713–11728). In this work, we implemented an algorithm and data structure that extends the process of assembly modelling to the automated generation of structural information about MOPs, enabling computational approaches to analyse trends in cavity and pore sizing. The structural geometries obtained from this work are semantically integrated as part of The World Avatar, a dynamic knowledge ecosystem.

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