(174f) Exploring Potential Covalent Organic Building Blocks Using High-Throughput Molecular Screening Framework

Covalent organic frameworks (COFs) are a type of crystalline porous polymers consisting of organic building blocks and reversible covalent linkage. There is remarkable interest in the designed synthesis of COFs given its applications in diverse areas such as molecular separation, catalysis, energy storage, and drug delivery. Identification of potential organic building blocks from a targeted tiling structure through reticular chemistry has proven to be experimentally feasible, however, the manual process might preclude the possibility of designing a complex structural tiling with simple organic building blocks.

In a recent Monte Carlo simulation-based study, we identified primary and secondary building block structures and chemical specificity required to self-assemble into Archimedean tilings with minimal errors. In this work, we present a computational approach to automatically identify the molecules that possess these structures and functionality; we then experimentally synthesize the corresponding COFs using these building blocks.

In particular, our approach enumerates a large number of planar molecules and scans through this database to find building blocks matching the targeted topology (having the right building block shape and containing the requisite functional groups). The process can be broken down into steps: (1) Generate/identify molecular scaffolds. (2) Add functional groups to molecular scaffolds using a cheminformatics based network generator; specifically, we apply functional groups (pairs) such as aldehyde, diol, boronic acid, which have been reported to form dynamic covalent bonds and COFs through reversible chemistries. (3) scan through all 2D molecules and select molecules that match the desired building block topology by computing angles between functional groups, identifying symmetric properties and type of functional groups. In the end, we assess the synthetic feasibility (and plausible synthesis routes) of promising building blocks through AI based tools.

Using this framework, we identified the optimal building blocks for a triporous COF, which we then verified through synthesis and experimental characterization. The computational techniques, the building blocks identified by our workflow, and the results of the experimental synthesis will be covered in the talk.