CAS: Computational Data-Driven Metal-Free Catalysts Discovery for Small Molecule Activation and Conversion

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In this project, funded by the Chemical Structure, Dynamics & Mechanisms-B Program of the Chemistry Division, Jingyun Ye of the Department of Chemistry and Biochemistry at Duquesne University aims to design and discover energy-saving, environmentally friendly, and low-cost metal-free catalysts for small molecule activation. Small molecule conversions such as the conversion of carbon dioxide to useful chemicals and fuels have the potential to reduce reliance on fossil carbon sources and build a more renewable carbon cycle. The approach taken here is based on frustrated Lewis pairs (FLPs). This project aims to construct an open access FLP database and to combine quantum mechanical modeling with data science and machine learning to accelerate the discovery of novel FLPs with targeted catalytic reactivity for small molecule activation and conversion. The proposed research will have significant educational and research opportunities for the next generation of researchers in the cross-disciplinary fields of chemistry, computational modeling, materials science, catalysis, data science, and artificial intelligence, with a particular focus on promoting the participation of members of underrepresented groups and women in science. Frustrated Lewis pairs are the simple combination of a bulky Lewis acid and a bulky Lewis base that are sterically precluded from reacting with each other. The unquenched Lewis acid and Lewis base sites are available to accept and donate electron density, respectively, providing a unique route to the activation and catalytic conversion of small molecules. The overall research goal of this project is to reveal the structure-activity relationships of FLPs for small molecule activation and the design of novel and efficient metal-free catalysts for energy conversion and environmental concerns via the combination of density functional theory, data science, and machine learning. The project objectives are to: (1) construct an open-access FLPs database that contains structural, electronic, and energetic data of FLPs and their activity toward small molecule activation; (2) identify the structure-activity relationships of FLPs for carbon dioxide hydrogenation and alkyne semi-hydrogenation using machine learning; and (3) design FLPs with targeted properties for small molecule activation and catalysis via deep learning.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.