Inverse Design of Metal-Organic Framework Catalysts

Objective

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 specific chemical transformation. However, the primary challenge is their near-infinite combinatorial design space resulting from the large variety of possible building blocks and the diverse ways they can be assembled. This complexity makes it extremely difficult to identify a MOF catalyst tailored to a given application. Computational modeling has emerged as a key tool to sort out this myriad of possibilities and guide experimental efforts. However, straightforward computational screening approaches, the most widely used strategy thus far, often prove impractical for exploring the vast combinatorial space of MOF catalysts. To address this challenge, the research project IMOCAT will devise a protocol to efficiently search the massive chemical space of possible MOFs which will enable the inverse design of new and better catalysts for selective ethylene oligomerization, an important reaction in the petrochemical industry. To this end, cutting-edge computational tools will be integrated with a genetic algorithm that allows for simultaneous optimization of MOF building blocks to achieve the desired activity-selectivity tradeoff. The overarching goal of the IMOCAT project is to derive pragmatic and experimentally relevant catalyst design principles by analyzing the lead MOF candidates. These principles will then guide experimental researchers in synthesizing MOF catalysts with improved performance for selective ethylene oligomerization.