Covalent organic frameworks (COFs) belong to an emerging class of organic materials with low density, high thermal stability, and stable porosity, with applications including molecular separations, catalysis, energy storage, semiconductors, drug delivery, and single-molecule sensors. They are constructed via dynamic covalent assembly of organic building-block (BB) molecules, a process wherein the BBs essentially polymerize to form an extended crystalline material. The COF ?universe,? i.e. the set of all possible COF structures, is vast and potentially comprises billions of potential candidates with varying pore size and geometry, chemical composition, and functionality. Only a small fraction of this ?universe? has been synthesized so far, although new COFs are continuously being reported. Nevertheless, there are still very few reported COFs with more than two types of pores, a critical shortcoming because COFs with combinations of pore sizes hold promise for enhancing catalysis under confinement, molecular separation processes, and gas storage applications. A prominent idea for designed synthesis of COFs, known as reticular chemistry, is to identify rigid BB molecules that can be uniquely assembled into the desired COF pattern via covalent bonds. While extremely useful, the related chemical design is often implemented manually, and is therefore not scalable to more complex topologies nor is it able to enumerate the vast space of COFs. In this context, the goal of this research is to develop a computational method to automatically generate synthetically feasible 2D covalent organic frameworks with multiple pore sizes and thereby guide experimental efforts to synthesize complex structures. Synthetic feasibility considers whether the building block(s) can be easily created using known chemistries and starting materials and easily assembled into the requisite crystalline COF structure.
This research brings together tools from cheminformatics, reaction network generation, advanced molecular simulations, and artificial intelligence to create an automated method to identify COFs that can be synthesized using easily available starting materials and proven organic chemistries. This method will be used to create a database of COFs with complex (in particular heteroporous) topology. Given a target structure, the method will identify the necessary building block structure and its chemical functionality via coarse-grained molecule-like patchy particle simulations. The resulting information will be used to generate potential molecular building blocks using a reinforcement learning-based biased automated network generation process. The synthetic complexity of these molecular building blocks will then be evaluated using cheminformatics tools and algorithms. For the most synthetically feasible molecules, their synthesis routes will be generated using the concept of retrosynthesis via automated network generation. The end result of this process will be a list of theoretically determined synthetically feasible COFs, their building blocks, and their synthesis routes. Such lists will be compiled for each tiling and ranked based on the synthesis scores. A few of the most promising COFs identified through this strategy will be verified experimentally in a bottom-up assembly involving synthesis of the building blocks and their assembly using orthogonal reaction chemistries. To integrate research and education, the PIs will employ the concept of student-led creation of original scientific research content as part of curricular training, or ?class sourcing,? by designing course projects wherein the cumulative expertise of the entire cohort of students is leveraged to identify strategies to synthesize new building blocks and thereby improve rules for network generation.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
