99 Research Summary: Using Data Science Tools to Understand Inhibitor Structure Impact on Non-Proportional Bifurcated FROMP Rates with Cleavable Comonomer DHF

Faculty Mentor: Matthew Sigman (Chemistry, University of Utah)

We present a data-driven workflow to assess the effects of monomer and inhibitor choices on the rate and pot life of Frontal Ring-Opening Metathesis Polymerization (FROMP) ruins the Grubbs generation II catalyst. A single-node decision tree classification model, based on a phosphine structural descriptor, was developed to differentiate between pot lifetimes. Additionally, a nonlinear kernel ridge regression model was employed to predict how inhibitor and comonomer selectivity influence the FROMP rate. The original data set has been expanded to include a more dynamic DHF range and additional monomer concentrations for pot life evaluation. This analysis offers criteria for selecting material networks, including cross-linked thermosets and degradable materials.