Employing Data Science Tools to Develop Reactions Using Heteroleptic Catalysts

University Of Utah, Salt Lake City UT

Investigators

Abstract

With the support of the Chemical Catalysis program in the Division of Chemistry, Professor Matthew Sigman of the University of Utah is studying the reactivity, mechanism, and synthetic application of heteroleptic catalysts. During this project, the Sigman group will investigate heteroleptic metal complexes catalysts that require two disparate ligand scaffolds for function on a single metal. A broad goal of the Sigman lab is the fundamental understanding of relationships between catalyst structure and function. In modern catalysis, ligands, and the catalysts they support, have evolved in complexity to meet synthetic demands. As such, the Sigman group has been at the forefront of applying data science in chemistry to better understand intricate structure-function relationships, contributing to advancements in reactivity and optimizations. By applying a unified set of data science tactics, including the use of physically meaningful molecular descriptors, this project will provide the broader community with new strategies for reaction development and enhanced methods for chemical synthesis. Ultimately, this work will enable independent tuning of each ligand's role to achieve novel selectivity and reactivity. This work is highly collaborative, which will not only provide robust professional development opportunities for the trainees involved but also enable our science to reach broader audiences. The Sigman group highly values training the upcoming generation of chemists. As part of this work, the group will dedicate time towards training incoming students and scholars in the field of data science in addition to developing and sharing open-source educational/science tools for the at-large community. With the support of the Chemical Catalysis program in the Division of Chemistry, Professor Matthew Sigman of the University of Utah is studying new strategies for organometallic reaction development while advancing methods for chemical synthesis. This will be achieved through the exploration of heteroleptic catalytic systems, in which two distinct ligands (with non-additive impacts on reactivity) are coordinated to a single metal center. Additionally, the integration of modern data science tools with rigorous synthetic data collection will help decode intricate structure-activity relationships. These relationships will be probed using physically interpretable molecular descriptors, carefully designed datasets for high-throughput experimentation, machine-learning optimization, and data analysis employing contemporary physical organic techniques. It is expected that this work will be most applicable to catalysis that proceeds via transition metal species with high oxidation states, and as a result will yield higher selectivity (site-, atropo-, and enantio-) in addition to novel reactivity with alternative metals. It is anticipated that the approach and strategies developed during this project will be broadly applicable to complex catalytic systems with non-intuitive reactivity patterns. 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.