Mapping Interfacial Water States on Functionalized Graphene: A Machine Learning-Augmented Approach to Uncover Design Principles for Tunable Water Transport

Abstract: Controlling water transport in nano-confined environments, such as functionalized graphene, is crucial for developing advanced materials with tailored properties. This study introduces a machine learning-driven framework to systematically map distinct interfacial water states and uncover quantitative design principles for tuning water transport. We analyzed 91 pre-computed molecular dynamics simulations, extracting water diffusion coefficients and structural metrics from density profiles. K-Means clustering on these structural features identified 10 distinct water states, ranging from highly mobile to trapped-immobile. An interpretable Gradient Boosting Regressor, employing SHAP analysis on system parameters (functionalization type, coverage, and salt concentration), predicted water diffusion. Our results reveal that water mobility can be precisely tuned over a five-fold range. Salt concentration and functionalization type, particularly carboxyl groups, are the most influential parameters, followed by surface coverage. Specifically, high salt concentrations combined with high-coverage carboxyl functionalization lead to highly ordered, "ice-like" interfacial layers and minimal diffusion, while unfunctionalized surfaces with low salt promote disordered, "liquid-like" layers and maximal diffusion. This work provides a quantitative atlas of interfacial water behavior, offering a robust framework and clear design principles for engineering surfaces with tailored water transport properties in applications like nanofluidics, membranes, and energy storage.

Subjects:	cond-mat.mtrl-sci; cs.LG
Cite as:	PX:2508.00080