Combined machine learning and atomistic simulations reveal multi-state hydration in cationic brushes in the presence of halide counterions

Abstract

In this communication, we employ a combination of all-atom molecular dynamics simulations and machine learning to establish the effect of different halide screening counterions (fluoride, chloride, bromide, and iodide ions) on the prevalence of two separate hydration states of the {N(CH₃)₃}⁺ functional group of the PMETA (([poly(2-(methacryloyloxy)ethyl) trimethylammonium) cationic brushes.