Hossein Ghorbanfekr^1,2*, ‪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.

Description

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.

Materials Cloud sections using this data

Files

File name	Size	Description
NNP.zip MD5md5:3a7d6db05c35f84be4c18a85b2555848	153.1 KiB	The parameters of the nanoconfined water neural network potential between stacked layers of hBN membranes.
MD.zip MD5md5:091775ba75defa584108ab4a95176b84	134.2 KiB	An example of LAMMPS input script on how to use our developed neural network potential.

License

Files and data are licensed under the terms of the following license: GNU General Public License v3.0 only.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

Nanoconfined water Two dimensional membranes Molecular dynamics simulations Neural network potentials