Inverse Design of Metal-Organic Framework Catalysts

Metal-organic frameworks (MOFs) are a class of crystalline porous materials composed of metal nodes connected by organic linkers.The atomically precise structures of most MOFs offer the opportunity to use them as catalysts that can be fine-tuned for a specificchemical transformation. However, the primary challenge is their near-infinite combinatorial design space resulting from the largevariety of possible building blocks and the diverse ways they can be assembled. This complexity makes it extremely difficult toidentify a MOF catalyst tailored to a given application. Computational modeling has emerged as a key tool to sort out this myriad ofpossibilities and guide experimental efforts. However, straightforward computational screening approaches, the most widely usedstrategy thus far, often prove impractical for exploring the vast combinatorial space of MOF catalysts. To address this challenge, theresearch project IMOCAT will devise a protocol to efficiently search the massive chemical space of possible MOFs which will enablethe inverse design of new and better catalysts for selective ethylene oligomerization, an important reaction in the petrochemicalindustry. To this end, cutting-edge computational tools will be integrated with a genetic algorithm that allows for simultaneousoptimization of MOF building blocks to achieve the desired activity-selectivity tradeoff. The overarching goal of the IMOCAT project isto derive pragmatic and experimentally relevant catalyst design principles by analyzing the lead MOF candidates. These principleswill then guide experimental researchers in synthesizing MOF catalysts with improved performance for selective ethyleneoligomerization.