(404i) Understanding Creep Suppression Mechanism in Polymer Nanocomposites through Machine Learning | AIChE

(404i) Understanding Creep Suppression Mechanism in Polymer Nanocomposites through Machine Learning


Pressly, J., University of Pennsylvania
Natarajan, B., 1ExxonMobil Research and Engineering Company
Colby, R., ExxonMobil Research and Engineering
Winey, K., University of Pennsylvania
Riggleman, R., University of Pennsylvania
While recent efforts have shown how local structure plays an essential role in the dynamic heterogeneity of homogeneous glass-forming materials, systems containing interfaces such as thin films or composite materials remain poorly understood. It is known that interfaces can perturb the molecular packing nearby, however, numerous studies show the dynamics are modified over a much larger range. Here, we examine the dynamics in polymer nanocomposites (PNCs) using a combination of simulations and experiments and quantitatively separate the role of polymer packing change from other effects on the dynamics, as a function of distance to nanoparticle surfaces. After showing good qualitative agreement between the simulations and experiments in glassy structure and creep compliance, we use a machine-learned structural field, softness, and decompose polymer dynamics in our simulated PNCs into structure-dependent and structure-independent processes. With this decomposition, the free energy barrier for polymer rearrangement can be described as a combination of packing-dependent and packing-independent barriers. We find both barriers are higher near nanoparticles and decrease with applied stress, quantitatively demonstrating that the slow interfacial dynamics are not solely due to polymer packing change, but also the change of structure-dynamics relationships. Finally, we present how this decomposition can be used to accurately predict strain-time creep curves for different PNCs from the static configuration, providing a microscopic picture of the glassy dynamics that leads to creep.