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Comparing equilibration schemes of high-molecular-weight polymer melts with topological indicators.

Luca TubianaHideki KobayashiRaffaello PotestioBurkhard DünwegKurt KremerPeter VirnauKostas Ch Daoulas
Published in: Journal of physics. Condensed matter : an Institute of Physics journal (2021)
Recent theoretical studies have demonstrated that the behaviour of molecular knots is a sensitive indicator of polymer structure. Here, we use knots to verify the ability of two state-of-the-art algorithms-configuration assembly and hierarchical backmapping-to equilibrate high-molecular-weight (MW) polymer melts. Specifically, we consider melts with MWs equivalent to several tens of entanglement lengths and various chain flexibilities, generated with both strategies. We compare their unknotting probability, unknotting length, knot spectra, and knot length distributions. The excellent agreement between the two independent methods with respect to knotting properties provides an additional strong validation of their ability to equilibrate dense high-MW polymeric liquids. By demonstrating this consistency of knotting behaviour, our study opens the way for studying topological properties of polymer melts beyond time and length scales accessible to brute-force molecular dynamics simulations.
Keyphrases
  • molecular dynamics simulations
  • machine learning
  • drug delivery
  • single molecule
  • molecular docking
  • deep learning
  • cancer therapy
  • monte carlo