We investigated the structural and dynamical properties of water confined within hexagonal boron nitride nanopores using molecular dynamics simulations with machine learning potentials trained on ab initio data. Density profiles reveal pronounced layering behavior, with a distinct bilayer structure observed within the 9-Å pore size. Diffusion coefficients exhibit non-monotonic dependence on pore size, with water confined in 10-Å pores exhibiting the highest value among the other pores considered. Water confined within the 9-Å pore shows the lowest diffusivity despite having the weakest hydrogen-bond network. This demonstrates that geometric confinement, rather than hydrogen bond strength, dominates water dynamics within the confined systems studied here. These findings demonstrate size-dependent confinement effects with a transition regime at approximately 10 Å, where relaxation of spatial constraints considerably enhances water mobility. Infrared spectra reveal confinement and interface-induced shifts and broadening of vibrational features, reflecting altered hydrogen-bond environments and dynamical heterogeneity across pore sizes. The use of machine-learned potentials enables an accurate description of polarization, hydrogen-bond fluctuations, and vibrational dynamics under confinement, which are not fully captured by conventional force fields. These combined results demonstrate a confinement-driven crossover governed by the interplay between geometry, interfacial chemistry, and hydrogen-bonding dynamics, providing key insight for predicting and tuning transport in nanofluidic and interfacial systems. Consistent with prior observations, layering appears to be primarily controlled by confinement geometry, while surface chemistry influences molecular orientation and interfacial ordering.
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Bui et al. (2026) studied this question.
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