<p>The ability to crosslink Metal-Organic Frameworks (MOFs) has recently been discovered as a flexible approach towards synthesizing MOF-templated “ideal network
polymers”. Crosslinking MOFs with rigid cross-linkers would allow the synthesis of
crystalline Covalent-Organic Frameworks (COFs) of so far unprecedented flexibility
in network topologies, far exceeding the conventional direct COF synthesis approach.
However, to date only flexible cross-linkers were used in the MOF crosslinking approach, since a rigid cross-linker would require an ideal fit between the MOF structure
and the cross-linker, which is experimentally extremely challenging, making in silico design mandatory. Here, we present an effective geometric method to find an ideal
MOF cross-linker pair by employing a high-throughput screening approach. The algorithm considers distances, angles, and arbitrary rotations to optimally match the
cross-linker inside the MOF structures. In a second, independent step, using Molecular Dynamics (MD) simulations we quantitatively confirmed all matches provided by
the screening. Our approach thus provides a robust and powerful method to identify
ideal MOF/Cross-linker combinations, which helped to identify several MOF-to-COF
candidate structures by starting from suitable libraries. The algorithms presented here
can be extended to other advanced network structures, such as mechanically interlocked
materials or molecular weaving and knots<br></p>
Machine Learning and High-throughput Computational Screening of Metal-organic Framework for Separation of Methane/ethane/propane
