Active Learning Guided Optimization of Frontal Ring-Opening Metathesis Polymerization via Alkylidene Modification

Frontal ring-opening metathesis polymerization (FROMP) offers an energy-efficient method for manufacturing high-performance thermoset resins. However, the background reaction attributed to ring-opening metathesis polymerization (ROMP) results in a complex trade-off between the resin shelf life─necessary for practical manufacturability─and the front velocity. Here, we study the influence of alkylidene ligand selection in Grubbs’ second-generation Ru-initiators on the kinetics of FROMP and background ROMP. We reveal that ligand identity differentially affects FROMP and background ROMP reactivity, enabling tunable control over pot life and front speed. Leveraging this insight, we use active learning with multiobjective Bayesian optimization to efficiently explore the FROMP resin design space and identify superior resin formulations. This work advances the rational design of FROMP resins, expanding the range of accessible formulations and accelerating the discovery of high-performance materials for energy-efficient manufacturing applications.