Temperature-pressure phase diagram of confined monolayer water/ice at first-principles accuracy with a machine-learning force field
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
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Original language | English |
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Article number | 4110 |
Journal / Publication | Nature Communications |
Volume | 14 |
Online published | 11 Jul 2023 |
Publication status | Published - 2023 |
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Link to Scopus | https://www.scopus.com/record/display.uri?eid=2-s2.0-85164449136&origin=recordpage |
Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(a595ac5d-f933-4b64-b7a9-d148c37ef541).html |
Abstract
Understanding the phase behaviour of nanoconfined water films is of fundamental importance in broad fields of science and engineering. However, the phase behaviour of the thinnest water film – monolayer water – is still incompletely known. Here, we developed a machine-learning force field (MLFF) at first-principles accuracy to determine the phase diagram of monolayer water/ice in nanoconfinement with hydrophobic walls. We observed the spontaneous formation of two previously unreported high-density ices, namely, zigzag quasi-bilayer ice (ZZ-qBI) and branched-zigzag quasi-bilayer ice (bZZ-qBI). Unlike conventional bilayer ices, few inter-layer hydrogen bonds were observed in both quasi-bilayer ices. Notably, the bZZ-qBI entails a unique hydrogen-bonding network that consists of two distinctive types of hydrogen bonds. Moreover, we identified, for the first time, the stable region for the lowest-density 4 ⋅ 82 monolayer ice (LD-48MI) at negative pressures (<−0.3 GPa). Overall, the MLFF enables large-scale first-principle-level molecular dynamics (MD) simulations of the spontaneous transition from the liquid water to a plethora of monolayer ices, including hexagonal, pentagonal, square, zigzag (ZZMI), and hexatic monolayer ices. These findings will enrich our understanding of the phase behaviour of the nanoconfined water/ices and provide a guide for future experimental realization of the 2D ices. © 2023, The Author(s).
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Citation Format(s)
Temperature-pressure phase diagram of confined monolayer water/ice at first-principles accuracy with a machine-learning force field. / Lin, Bo; Jiang, Jian; Zeng, Xiao Cheng et al.
In: Nature Communications, Vol. 14, 4110, 2023.
In: Nature Communications, Vol. 14, 4110, 2023.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
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