AI Article Synopsis

  • - The study explores the behavior of strongly charged polyelectrolytes (PEs), particularly sodium polystyrene sulfonate (NaPSS), focusing on how chain length, concentration, and ionic strength affect viscosity, which is vital for various applications.
  • - Molecular dynamics simulations often struggle with predicting viscosities, so researchers employed Bayesian optimization to create coarse-grained models of NaPSS that accurately reflect both its structure and dynamics in water, using an explicit solvent approach.
  • - The new coarse-grained model shows strong agreement with existing simulations and experiments for short chains and is effective for larger chains and varying concentrations, offering a machine-learned tool for studying and designing polyelectrolytes with desirable structural

Article Abstract

Strongly charged polyelectrolytes (PEs) demonstrate complex solution behavior as a function of chain length, concentrations, and ionic strength. The viscosity behavior is important to understand and is a core quantity for many applications, but aspects remain a challenge. Molecular dynamics simulations using implicit solvent coarse-grained (CG) models successfully reproduce structure, but are often inappropriate for calculating viscosities. To address the need for CG models which reproduce viscoelastic properties of one of the most studied PEs, sodium polystyrene sulfonate (NaPSS), we report our recent efforts in using Bayesian optimization to develop CG models of NaPSS which capture both polymer structure and dynamics in aqueous solutions with explicit solvent. We demonstrate that our explicit solvent CG NaPSS model with the ML-BOP water model [Chan et al. Nat Commun 10, 379 (2019)] quantitatively reproduces NaPSS chain statistics and solution structure. The new explicit solvent CG model is benchmarked against diffusivities from atomistic simulations and experimental specific viscosities for short chains. We also show that our Bayesian-optimized CG model is transferable to larger chain lengths across a range of concentrations. Overall, this work provides a machine-learned model to probe the structural, dynamic, and rheological properties of polyelectrolytes such as NaPSS and aids in the design of novel, strongly charged polymers with tunable structural and viscoelastic properties.

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http://dx.doi.org/10.1140/epje/s10189-023-00355-xDOI Listing

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