Targeted classification of metal-organic frameworks in the Cambridge structural database (CSD)

Area of Science:

• Materials Science
• Crystallography
• Computational Chemistry

Background:

• Systematic exploration of metal-organic frameworks (MOFs) based on specific chemical or structural features is lacking.
• MOF properties like surface chemistry and stability are crucial for molecular interactions and applications.
• Existing methods do not efficiently facilitate targeted MOF searches based on key characteristics.

Purpose of the Study:

• To develop algorithms for classifying MOFs into subgroups based on critical chemical and physical attributes.
• To create tools for efficient browsing and searching of targeted MOF families within the Cambridge Crystallographic Data Centre (CCDC) software.
• To provide a dynamic database and interactive explorer for MOF research and design.

Main Methods:

• Development of algorithms to categorize MOFs by metal-cluster, network/pore dimensionality, surface chemistry, and chirality.
• Integration of these algorithms into CCDC software for user-friendly searching.
• Creation of an interactive web-based data explorer for accessing classified MOF data.

Main Results:

• A systematic classification of MOFs based on key chemical and physical features has been established.
• New computational tools allow researchers to efficiently search for specific MOF families.
• An interactive data explorer provides access to the classified MOF data for diverse applications.

Conclusions:

• The developed algorithms and tools facilitate targeted exploration and design of MOFs.
• This approach enables both experimentalists and computational users to identify specific MOF classes.
• The toolbox supports future research and development of MOFs for various applications, including hydrogen storage.