Minisymposium Presentation

The computational study of complex polymeric materials is a very challenging field, due to the broad spectrum of the underlying length and time scales. Here, we present a hierarchical multi-scale methodology for predicting the macroscopic properties of polymer-based nanostructured systems, which involves multi-scale simulations and Machine Learning algorithms. The simulations involve atomistic, coarse-grained, as well as continuum models. The coarse-grained (CG) models are derived through a “bottom-up” data-driven strategy, using information from the detailed atomistic scale, for the given chemistry. The systematic linking between the atomistic and the chemistry-specific CG scale, allows the study of a broad range of molecular weights, for specific polymers, without any adjustable parameter. At the same time, machine learning (ML) algorithms have been developed to re-introduce atomic detail in the CG scale, and thus obtaining atomistic configurations of high molecular weight polymers. The proposed hierarchical computational scheme allows the study of macromolecular systems, of high molecular weight, over a broad range of time scales, from a few fs up to several ms and the prediction of their (structural, dynamical, rheological, etc.) properties. As examples, we present results concerning the properties of various systems; polymer melts, polymer thin films and graphene-based polymer nanocomposites.