Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening.

ABSTRACT: By combining metal nodes and organic linkers, an infinite number of metal organic frameworks (MOFs) can be designed in silico. Therefore, when making new databases of such hypothetical MOFs, we need to ensure that they not only contribute toward the growth of the count of structures but also add different chemistries to the existing databases. In this study, we designed a database of ∼20,000 hypothetical MOFs, which are diverse in terms of their chemical design space─metal nodes, organic linkers, functional groups, and pore geometries. Using machine learning techniques, we visualized and quantified the diversity of these structures. We find that on adding the structures of our database, the overall diversity metrics of hypothetical databases improve, especially in terms of the chemistry of

SUBMITTER: Majumdar S 

PROVIDER: S-EPMC8719320 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature