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Temperature-pressure phase diagram of confined monolayer water/ice at first-principles accuracy with a machine-learning force field
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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10336112/ https://www.ncbi.nlm.nih.gov/pubmed/37433823 http://dx.doi.org/10.1038/s41467-023-39829-z |
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author | Lin, Bo Jiang, Jian Zeng, Xiao Cheng Li, Lei |
author_facet | Lin, Bo Jiang, Jian Zeng, Xiao Cheng Li, Lei |
author_sort | Lin, Bo |
collection | PubMed |
description | 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 [Formula: see text] 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. |
format | Online Article Text |
id | pubmed-10336112 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-103361122023-07-13 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 Li, Lei Nat Commun Article 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 [Formula: see text] 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. Nature Publishing Group UK 2023-07-11 /pmc/articles/PMC10336112/ /pubmed/37433823 http://dx.doi.org/10.1038/s41467-023-39829-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Lin, Bo Jiang, Jian Zeng, Xiao Cheng Li, Lei Temperature-pressure phase diagram of confined monolayer water/ice at first-principles accuracy with a machine-learning force field |
title | Temperature-pressure phase diagram of confined monolayer water/ice at first-principles accuracy with a machine-learning force field |
title_full | Temperature-pressure phase diagram of confined monolayer water/ice at first-principles accuracy with a machine-learning force field |
title_fullStr | Temperature-pressure phase diagram of confined monolayer water/ice at first-principles accuracy with a machine-learning force field |
title_full_unstemmed | Temperature-pressure phase diagram of confined monolayer water/ice at first-principles accuracy with a machine-learning force field |
title_short | Temperature-pressure phase diagram of confined monolayer water/ice at first-principles accuracy with a machine-learning force field |
title_sort | temperature-pressure phase diagram of confined monolayer water/ice at first-principles accuracy with a machine-learning force field |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10336112/ https://www.ncbi.nlm.nih.gov/pubmed/37433823 http://dx.doi.org/10.1038/s41467-023-39829-z |
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