Understanding the Geometric Diversity of Inorganic and Hybrid Frameworks through Structural Coarse-Graining

TL;DR

This paper introduces a systematic, quantitative framework combining atom-density similarity and machine learning to compare and map diverse inorganic and hybrid frameworks, revealing structural relationships and linking them to microscopic properties.

Contribution

It presents a novel approach for coarse-graining atomic structures, enabling the comparison of vastly different chemical systems through a unified structure map.

Findings

Created a two-dimensional map of tetrahedral AB2 networks

Linked structural relationships to properties like heterogeneity and density

Demonstrated the method's ability to compare inorganic and hybrid frameworks

Abstract

Much of our understanding of complex structures is based on simplification: for example, metal-organic frameworks are often discussed in the context of "nodes" and "linkers", allowing for a qualitative comparison with simpler inorganic structures. Here we show how such an understanding can be obtained in a systematic and quantitative framework, by combining atom-density based similarity (kernel) functions and unsupervised machine learning with the long-standing idea of "coarse-graining" atomic structure. We demonstrate how the latter enables a comparison of vastly different chemical systems, and use it to create a unified, two-dimensional structure map of experimentally known tetrahedral AB2 networks - including clathrate hydrates, zeolitic imidazolate frameworks (ZIFs), and diverse inorganic phases. The structural relationships that emerge can then be linked to microscopic properties of interest, which we exemplify for structural heterogeneity and tetrahedral density.