Atomistic and Mesoscopic Modeling of Structure-Property Relations in Polymers
Multi-scale strategies to obtain macroscopic properties from atomic-scale models.
Quantum and statistical mechanics based molecular modeling approaches for understanding structure-property relations and tailoring material production processes have achieved increasing prominence in academic research and industrial practice. These molecular approaches have been enabled not only by unprecedented growth in computer power, but by new, more efficient theoretical and computational methods and algorithms. The broad spectrum of length and time scales governing structure and dynamics in real-life materials demands advanced #multiscale modeling strategies, in order to connect atomic-level structure and physical processes with #macroscale material behavior. In this talk, Professor Doros Theodorou presents three examples in which these disparate length and time scales have been successfully bridged, and in which molecular modelling has illuminated structure-property relations in polymeric #materials:
(a) Prediction of linear and nonlinear rheological properties of high-molecular weight #polymer melts, such as polyethylene and cis-1,4 polyisoprene. This study employed hybrid particle-field #mesoscopic simulations employing slip-springs to represent polymer chain entanglements, which were parameterized on the basis of #atomistic calculations;
(b) Tracking of structural relaxations in polymer glasses, such as polystyrene. Relaxations were modeled as a sequence of elementary transitions between basins on their energy hypersurfaces, using transition rate constants computed from atomistic infrequent-event analyses;
(c) Quantifaction of the conditions for local interfacial failure in epoxy/graphene or graphene oxide #interfaces. This study employed stress-controlled nonequilibrium molecular dynamics simulations.
For each example above, Prof. Theodorou will outline how computationally tractable scale-hopping algorithms, based on rigorous statistical mechanical principles were devised to meet the challenges of long time and length scales. These computational studies produced property predictions validated by experimental measurements, and which elucidate molecular-level processes that are critical to materials design.
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