Employing Data Science Tools to Develop Reactions Using Heteroleptic Catalysts

NSF Award Search · 01002526DB NSF RESEARCH & RELATED ACTIVIT · $600,000 · view on nsf.gov ↗

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

Key facts

NSF award ID
2452790
Awardee
University of Utah (UT)
SAM.gov UEI
LL8GLEVH6MG3
PI
Matthew S Sigman
Primary program
01002526DB NSF RESEARCH & RELATED ACTIVIT
All programs
Artificial Intelligence (AI)
Estimated total
$600,000
Funds obligated
$600,000
Transaction type
Standard Grant
Period
10/01/2025 → 09/30/2028