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UID:submissions.pasc-conference.org_PASC24_sess167_msa291@linklings.com
SUMMARY:From Atomistic to Coarse-Grained Models of Complex Systems: Physic
 s-Based or Data-Driven Approaches?
DESCRIPTION:Minisymposium\n\nVagelis Harmandaris (The Cyprus Institute, Un
 iversity of Crete)\n\nThe computational study of complex polymeric materia
 ls is a very challenging field, due to the broad spectrum of the underlyin
 g length and time scales. Here, we present a hierarchical multi-scale meth
 odology for predicting the macroscopic properties of polymer-based nanostr
 uctured systems, which involves multi-scale simulations and Machine Learni
 ng 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 atom
 istic scale, for the given chemistry. The systematic linking between the a
 tomistic 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 de
 veloped to re-introduce atomic detail in the CG scale, and thus obtaining 
 atomistic configurations of high molecular weight polymers. The proposed h
 ierarchical computational scheme allows the study of macromolecular system
 s, 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, r
 heological, etc.) properties. As examples, we present results concerning t
 he properties of various systems; polymer melts, polymer thin films and gr
 aphene-based polymer nanocomposites.\n\nDomain: Chemistry and Materials, L
 ife Sciences, Physics\n\nSession Chair: Andreas Vitalis (University of Zur
 ich)
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