Abstract
Ring-opening enthalpy (ΔH(ROP)) is a fundamental thermodynamic quantity controlling the polymerization and depolymerization of an important class of recyclable polymers, namely, those created from ring-opening polymerization (ROP). Highly accurate first-principles-based computational methods to compute ΔH(ROP) are computationally too demanding to efficiently guide the design of depolymerizable polymers. In this work, we develop a generalizable machine-learning model that was trained on experimental measurements and reliably computed simulation results of ΔH(ROP) (the latter provides a pathway to systematically increase the chemical diversity of the data). Predictions of ΔH(ROP) using this machine-learning model require essentially no time while the prediction accuracy is about ∼8 kJ/mol, approaching the well-known chemical accuracy. We hope that this effort will contribute to the future development of new depolymerizable polymers.