Combined Machine Learning and Molecular Dynamics Reveal Two States of Hydration of a Single Functional Group of Cationic Polymeric Brushes

R Ishraaq and TS Akash and S Das, MACROMOLECULES, 57, 5300-5312 (2024).

DOI: 10.1021/acs.macromol.3c02539

The state of hydration of a macromolecular system regulates a plethora of different properties of such a system. In this article, we develop a novel machine learning (ML) approach, based on the unsupervised clustering algorithm, for probing the hydration behavior of the N(CH3)(3)(+) functional group of the PMETAC poly(2-(methacryloyloxy)ethyl trimethylammonium chloride polyelectrolyte (PE) brush system. The PE brushes and brush-supported water molecules and counterions (chloride ions) are first described using all-atom molecular dynamics (MD) simulations. The simulation data is subsequently used in our ML framework to identify that (1) the N(CH3)(3)(+) functional groups of the PMETAC brushes have two distinct hydration states, with one state (state 1) being characterized by less structured water molecules and the other state (state 2) being characterized by more structured water molecules and (2) an enhancement in the brush grafting density leads to the progressive dissappearance of state 2. An increase in the grafting density increases the number of chloride counterions in a given volume around the N(CH3)(3)(+) functional group and increases the number of shared water molecules between N(CH3)(3)(+) and Cl-. The chloride counterions are associated with a hydration layer with much less structured water molecules. Therefore, with an increase in the grafting density, an increase in the percentage of shared water molecules leads to the prevalence of the hydration state of the N(CH3)(3)(+) moiety with less structured water molecules. Finally, we explain how the present findings are commensurate with two key previous related results, namely, a significantly large chloride ion mobility inside the PMETAC brush layer and the N(CH3)(3)(+)-Cl- average distance remaining independent of the PMETAC brush grafting density. We anticipate that the combined ML-MD- simulation approach proposed in this study can be adapted to probe other soft matter systems to reveal new insights into the underlying mechanisms of emergent phenomenon.

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