Identification Schemes for Metal–Organic Frameworks To Enable Rapid Search and Cheminformatics Analysis
Source abstract
Abstract
The modular nature of metal–organic frameworks (MOFs) leads to a very large number of possible structures. High-throughput computational screening has led to a rapid increase in property data that has enabled several potential applications for MOFs, including gas storage, separations, catalysis, and other fields. Despite their rich chemistry, MOFs are typically named using an ad hoc approach, which can impede their searchability and the discovery of broad insights. In this article, we develop two systematic MOF identifiers, coined MOFid and MOFkey, and algorithms for deconstructing MOFs into their building blocks and underlying topological network. We review existing cheminformatics formats for small molecules and address the challenges of adapting them to periodic crystal structures. Our algorithms are distributed as open-source software, and we apply them here to extract insights from several MOF databases. Through the process of designing MOFid and MOFkey, we provide a perspective on opportunities for the community to facilitate data reuse, improve searchability, and rapidly apply cheminformatics analyses.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Metal-Organic Frameworks: Synthesis and Applications · Machine Learning in Materials Science · Crystallography and molecular interactions
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
IMDEA Materials Institute, C/Eric Kandel 2, 2890 6 Getafe, Madrid, Spain
Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States
Department of Chemistry, University of California, Berkeley, California 94720, United States
Department of Chemistry and Chemical Theory Center, University of Minnesota, 207 Pleasant Street SE, Minneapolis, Minnesota 55455, United States
Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, Minnesota 55455, United States
Department of Chemistry and Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3H6, Canada
Vector Institute for Artificial Intelligence, Toronto, Ontario M5S 1M1, Canada
Canadian Institute for Advanced Research (CIFAR) Senior Fellow, Toronto, Ontario M5S 1M1, Canada
Funding
U.S. Department of Energy · U.S. Department of Defense · National Science Foundation
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