Machine learning quantum-chemical bond scission in thermosets under extreme deformation

Despite growing interest in polymers under extreme conditions, most atomistic molecular dynamics simulations cannot describe the bond scission events underlying failure modes in polymer networks undergoing large strains. In this work, we propose a physics-based machine learning approach that can detect and perform bond breaking with near quantum-chemical accuracy on-the-fly in atomistic simulations. Particularly, we demonstrate that by coarse-graining highly correlated neighboring bonds, the prediction accuracy can be dramatically improved. Compared to existing quantum mechanics/molecular mechanics (QM/MM) methods, our approach is approximately two orders of magnitude more efficient and exhibits improved sensitivity towards rare bond breaking events at low strain. The proposed bond breaking molecular dynamics scheme enables fast and accurate modeling of strain hardening and material failure in polymer networks, and can accelerate the design of polymeric materials under extreme conditions.