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

Lin，Bo1; Jiang，Jian2,3; Zeng，Xiao Cheng2,3; Li，Lei1

2023-12-01

10.1038/s41467-023-39829-z

Nature Communications   Impact Factor & Quartile

2041-1723

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 ⋅ 8 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.

SCI

English

NI Journal Papers ; NI论文

First ; Corresponding

National Key Ramp;D Program of China[2022YFA1503102] ; Guangdong Provincial Key Laboratory Program from the Department of Science and Technology of Guangdong Province[2021B1212040001] ; National Natural Science Foundation of China[22179058] ; Shenzhen fundamental research funding[JCYJ20210324115809026] ; GRF grant of the Research Grants Council of Hong Kong[11204123]

Science & Technology - Other Topics

Multidisciplinary Sciences

WOS:001037937100001

NATURE PORTFOLIO

2-s2.0-85164449136

Scopus

1.Guangdong Provincial Key Laboratory of Functional Oxide Materials and Devices,Department of Materials Science and Engineering,Southern University of Science and Technology,Shenzhen,Guangdong,518055,China
2.Department of Materials Science and Engineering,City University of Hong Kong,Kowloon,999077,Hong Kong
3.Department of Chemistry,University of Nebraska-Lincoln,Lincoln,68588,United States

Lin，Bo,Jiang，Jian,Zeng，Xiao Cheng,et al. Temperature-pressure phase diagram of confined monolayer water/ice at first-principles accuracy with a machine-learning force field[J]. Nature Communications,2023,14(1).

Lin，Bo,Jiang，Jian,Zeng，Xiao Cheng,&Li，Lei.(2023).Temperature-pressure phase diagram of confined monolayer water/ice at first-principles accuracy with a machine-learning force field.Nature Communications,14(1).

Lin，Bo,et al."Temperature-pressure phase diagram of confined monolayer water/ice at first-principles accuracy with a machine-learning force field".Nature Communications 14.1(2023).