Automated Assembly Modeling of Metal–Organic Polyhedra

Abstract

Assembly modeling has been achieved in knowledge AI systems for the automated inference of new and rational metal–organic polyhedra (J. Am. Chem. Soc. 2022, 144, 26, 11713–11728). This work presents an algorithm and data structure that extends the process of assembly modeling to the automated generation of structural information about metal–organic polyhedra, enabling automation of computational approaches to analyse trends in cavity and pore sizing. Distinct from string-based tools for purely organic cages, the workflow positions inorganic, organic, and hybrid chemical building units directly in 3-D and outputs geometries suitable for higher-level geometry optimization calculations in one step. The structural geometries obtained from this work are semantically integrated as part of The World Avatar, a dynamic knowledge ecosystem.