MOFGalaxyNet: a social network analysis for predicting guest accessibility in metal–organic frameworks utilizing graph convolutional networks

Journal of Cheminformatics

Research

A. D. Dinga Wonanke¹  Christof Wöll¹  Mehrdad Jalali² 

[1] Institute of Functional Interfaces (IFG), Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen, Germany;Institute of Functional Interfaces (IFG), Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen, Germany;Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen, Germany;

关键词: Metal–Organic Frameworks (MOF);    Social networking;    Machine learning;    Materials properties;    Guest accessibility;    MOFGalaxyNet;    Graph convolutional network (GCN);   

DOI  :  10.1186/s13321-023-00764-2

 received in 2023-06-29, accepted in 2023-09-23,  发布年份 2023

来源: Springer

【 摘 要 】

Metal–organic frameworks (MOFs), are porous crystalline structures comprising of metal ions or clusters intricately linked with organic entities, displaying topological diversity and effortless chemical flexibility. These characteristics render them apt for multifarious applications such as adsorption, separation, sensing, and catalysis. Predominantly, the distinctive properties and prospective utility of MOFs are discerned post-manufacture or extrapolation from theoretically conceived models. For empirical researchers unfamiliar with hypothetical structure development, the meticulous crystal engineering of a high-performance MOF for a targeted application via a bottom-up approach resembles a gamble. For example, the precise pore limiting diameter (PLD), which determines the guest accessibility of any MOF cannot be easily inferred with mere knowledge of the metal ion and organic ligand. This limitation in bottom-up conceptual understanding of specific properties of the resultant MOF may contribute to the cautious industrial-scale adoption of MOFs.Consequently, in this study, we take a step towards circumventing this limitation by designing a new tool that predicts the guest accessibility—a MOF key performance indicator—of any given MOF from information on only the organic linkers and the metal ions. This new tool relies on clustering different MOFs in a galaxy-like social network, MOFGalaxyNet, combined with a Graphical Convolutional Network (GCN) to predict the guest accessibility of any new entry in the social network. The proposed network and GCN results provide a robust approach for screening MOFs for various host–guest interaction studies.

【 授权许可】

CC BY   
© Springer Nature Switzerland AG 2023

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