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Insights into water permeation through hBN nanocapillaries by ab initio machine learning molecular dynamics simulations

Hossein Ghorbanfekr1,2*, ‪Jörg Behler3, François M. Peeters2

1 Data Science Hub, Flemish Institute for Technological Research (VITO), 2400 Mol, Belgium

2 Departement Fysica, Universiteit Antwerpen, 2020 Antwerpen, Belgium

3 Institut für Physikalische Chemie, Theoretische Chemie, Universität Göttingen, 37077 Göttingen, Germany

* Corresponding authors emails: hossein.ghorbanfekrkalashami@uantwerpen.be
DOI10.24435/materialscloud:m7-09 [version v1]

Publication date: Aug 16, 2020

How to cite this record

Hossein Ghorbanfekr, ‪Jörg Behler, François M. Peeters, Insights into water permeation through hBN nanocapillaries by ab initio machine learning molecular dynamics simulations, Materials Cloud Archive 2020.95 (2020), doi: 10.24435/materialscloud:m7-09.


Water permeation between stacked layers of hBN sheets forming 2D nanochannels is investigated using large-scale ab initio-quality molecular dynamics simulations. A high-dimensional neural network potential trained on density functional theory calculations is employed. We simulate water in van der Waals nanocapillaries and study the impact of nanometric confinement on the structure and dynamics of water using both equilibrium and nonequilibrium methods. At an interlayer distance of 10.2 Å confinement induces a first-order phase transition resulting in a well-defined AA-stacked bilayer of hexagonal ice. In contrast, for h < 9 Å, the 2D water monolayer consists of a mixture of different locally ordered patterns of squares, pentagons, and hexagons. We found a significant change in the transport properties of confined water, particularly for monolayer water where the water−solid friction coefficient decreases to half and the diffusion coefficient increases by a factor of 4 as compared to bulk water. Accordingly, the slip-velocity is found to increase under confinement and we found that the overall permeation is dominated by monolayer water adjacent to the hBN membranes at extreme confinements. We conclude that monolayer water in addition to bilayer ice has a major contribution to water transport through 2D nanochannels.

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File name Size Description
153.1 KiB The parameters of the nanoconfined water neural network potential between stacked layers of hBN membranes.
134.2 KiB An example of LAMMPS input script on how to use our developed neural network potential.


Files and data are licensed under the terms of the following license: GNU General Public License v3.0 only.
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External references

Journal reference (Paper in which the method is described)


Nanoconfined water Two dimensional membranes Molecular dynamics simulations Neural network potentials

Version history:

2020.95 (version v1) [This version] Aug 16, 2020 DOI10.24435/materialscloud:m7-09