Accelerated Scheme to Predict Ring-Opening Polymerization Enthalpy: Simulation-Experimental Data Fusion and Multitask Machine Learning
Aubrey Toland, Tran Doan Huan, Lihua Chen, Yinghao Li, Chao Zhang, Will R. Gutekunst, Rampi Ramprasad
Abstract
High Resolution Image Download MS PowerPoint Slide Ring-opening enthalpy (Δ H 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 Δ H 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 Δ H ROP (the latter provides a pathway to systematically increase the chemical diversity of the data). Predictions of Δ H 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.