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Polyelectrolytes, macromolecules with ionizable groups, play a critical role in applications ranging from energy storage and drug delivery to adhesives, owing to their strong interactions with ionic solutes and water. Despite their widespread utility, an atomistic understanding of how polyelectrolytes interact with ions remains incomplete, limiting the ability to precisely control their conformation and functional properties. To bridge this knowledge gap, we conducted molecular dynamics simulations of two representative polyelectrolytes, poly(vinylbenzyl trimethylammonium chloride) (PVBTACl) and sodium polystyrene sulfonate (NaPSS), across varying salt concentrations. We observed distinct salt-responsive behaviors: as the salt (NaCl) concentration increases from 0 to 2 M, the radius of gyration (Rg) of NaPSS decreases, indicating polymer compaction, while PVBTACl remains relatively unaffected. When the salt concentration is further increased to 6 M, PVBTACl undergoes significant collapse, whereas NaPSS remains in a compact state with minimal further conformational change. The difference in the salt-responsive behavior results from the local counterion structures, where the counterions of PVBTACl are less ordered than those of NaPSS. We further examined the PVBTACl/NaPSS complex to assess deviations from the behavior of isolated polymers, revealing enhanced association in contrast to the conventionally observed dissociation at the high salt concentration. Experimental transmittance measurements of equimolar PVBTACl/NaPSS mixtures across increasing salt concentrations confirmed stable complexation behavior under high-salt conditions, supporting the simulation-based observations of persistent association between PVBTACl and NaPSS. This study offers a mechanistic understanding of salt-induced conformational changes, providing design principles for tuning polyelectrolyte properties in functional materials. 

Graft polymers are promising in energy and biomedical applications. However, the diverse architectures make it challenging to establish their structure–property relationships. We systematically investigate how backbone and side-chain architectures influence four key properties: glass transition temperature (Tg), self-diffusion coefficient (D), radius of gyration (Rg), and packing density (ρ). Using molecular dynamics simulations, we analyze a dataset of 500 graft polymers with randomly positioned side chains. Tg and D exhibit decoupled relationships due to the distinct topological effects. Furthermore, we develop dense neural networks (DNNs) and convolutional neural networks (CNNs) to pave the way to polymer design with desired properties.