Using Data Science Tools to Reveal and Understand Subtle Relationships of Inhibitor Structure in Frontal Ring-Opening Metathesis Polymerization

The rate of frontal ring-opening
metathesis polymerization
(FROMP)
using the Grubbs generation II catalyst is impacted by both the concentration
and choice of monomers and inhibitors, usually organophosphorus derivatives.
Herein we report a data-science-driven workflow to evaluate how these
factors impact both the rate of FROMP and how long the formulation
of the mixture is stable (pot life). Using this workflow, we built
a classification model using a single-node decision tree to determine
how a simple phosphine structural descriptor (Vbur‑near) can bin long versus short pot life. Additionally,
we applied a nonlinear kernel ridge regression model to predict how
the inhibitor and selection/concentration of comonomers impact the
FROMP rate. The analysis provides selection criteria for material
network structures that span from highly cross-linked thermosets to
non-cross-linked thermoplastics as well as degradable and nondegradable
materials.