Jalali Mehrdad, Wonanke A D Dinga, Wöll Christof
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.
J Cheminform. 2023 Oct 11;15(1):94. doi: 10.1186/s13321-023-00764-2.
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.
金属有机框架（MOFs）是由金属离子或簇与有机实体复杂连接而成的多孔晶体结构，具有拓扑多样性和轻松的化学灵活性。这些特性使其适用于吸附、分离、传感和催化等多种应用。主要地，MOFs的独特性质和潜在用途是在制造后或从理论构想模型推断出来的。对于不熟悉假设结构开发的实证研究人员来说，通过自下而上的方法为目标应用精心设计高性能MOF就像是一场赌博。例如，仅知道金属离子和有机配体，很难轻易推断出决定任何MOF客体可及性的精确孔限直径（PLD）。对所得MOF特定性质的自下而上概念理解中的这种限制可能导致MOFs在工业规模上的谨慎采用。因此，在本研究中，我们朝着克服这一限制迈出了一步，设计了一种新工具，该工具仅根据有机连接体和金属离子的信息预测任何给定MOF的客体可及性——一种MOF关键性能指标。这种新工具依赖于在类似星系的社交网络MOFGalaxyNet中对不同的MOF进行聚类，并结合图形卷积网络（GCN）来预测社交网络中任何新条目的客体可及性。所提出的网络和GCN结果为筛选用于各种主客体相互作用研究的MOF提供了一种强大的方法。
