Temperature-pressure phase diagram of confined monolayer water/ice at first-principles accuracy with a machine-learning force field

Bo Lin, Jian Jiang, Xiao Cheng Zeng*, Lei Li*

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

40 Citations (Scopus)
46 Downloads (CityUHK Scholars)

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).
Original languageEnglish
Article number4110
JournalNature Communications
Volume14
Online published11 Jul 2023
DOIs
Publication statusPublished - 2023

Funding

L.L. is supported by the National Key R&D Program of China (No. 2022YFA1503102), Guangdong Provincial Key Laboratory Program (2021B1212040001) from the Department of Science and Technology of Guangdong Province, the National Natural Science Foundation of China (No. 22179058), and Shenzhen fundamental research funding (JCYJ20210324115809026). Computational resources were provided by the Centre for Computational Science and Engineering of the Southern University of Science and Technology. X.C.Z. acknowledges the support by Hong Kong Global STEM Professorship Scheme and by the GRF grant (11204123) of the Research Grants Council of Hong Kong.

Publisher's Copyright Statement

  • This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

RGC Funding Information

  • RGC-funded

Fingerprint

Dive into the research topics of 'Temperature-pressure phase diagram of confined monolayer water/ice at first-principles accuracy with a machine-learning force field'. Together they form a unique fingerprint.

Cite this