(475g) Explicit Solvent Machine-Learned Coarse-Grained Model of Polyelectrolytes to Capture Polymer Structure and Dynamics

Polyelectrolytes span a wide range of applications including biology (e.g., DNA), thickeners, water treatment, and drug treatments. For strongly charged polyelectrolytes, complex solution behavior is observed in simulations and experiments as a function of chain length, concentrations, and ionic strength versus uncharged polymers. In particular, the dynamics of strongly charged polyelectrolytes presents an important challenge. In order to address the viscosity of one of the most studied polyelectrolytes, sodium polystyrene sulfonate (NaPSS), there is a need for an accurate coarse-grained model with explicit solvent. In this talk, we present our recent efforts in using machine learning models such as Bayesian optimization and neural networks to develop coarse-grained (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)] quantitively reproduces NaPSS chain statistics and solution structure measured in terms of radii of gyration, end-to-end distances, and radial distribution functions validated by previous experiments and simulations. The new explicit solvent CG model is benchmarked against diffusivities from atomistic simulations and experimental specific viscosities for short chains. We will present results of the chain dynamics and rheological properties using the new CG model for a range of polymer concentrations and lengths. Overall, this work provides a computationally efficient machine-learned model to probe the structural, dynamic, and rheological properties of polyelectrolytes such as NaPSS, and aides in the design of novel, strongly charged polymers with tunable structural and viscoelastic properties.