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Identifying porous cage subsets in the Cambridge Structural Database using topological data analysis.

ABSTRACT: As rationally designable materials, the variety and number of synthesised metal-organic cages (MOCs) and organic cages (OCs) are expected to grow in the Cambridge Structural Database (CSD). In this regard, two of the most important questions are, which structures are already present in the CSD and how can they be identified? Here, we present a cage mining methodology based on topological data analysis and a combination of supervised and unsupervised learning that led to the derivation of - to the best of our knowledge - the first and only MOC dataset of 1839 structures and the largest experimental OC dataset of 7736 cages, as of March 2022. We illustrate the use of such datasets with a high-throughput screening of MOCs and OCs for xenon/krypton separation, important gases in multiple industries, including healthcare.

SUBMITTER: Li A

PROVIDER: S-EPMC9682994 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Identifying porous cage subsets in the Cambridge Structural Database using topological data analysis.

Li Aurelia A Bueno-Perez Rocio R Fairen-Jimenez David D

Chemical science 20221031 45

As rationally designable materials, the variety and number of synthesised metal-organic cages (MOCs) and organic cages (OCs) are expected to grow in the Cambridge Structural Database (CSD). In this regard, two of the most important questions are, which structures are already present in the CSD and how can they be identified? Here, we present a cage mining methodology based on topological data analysis and a combination of supervised and unsupervised learning that led to the derivation of - to th ...[more]

PMID: 36507160

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