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Accurate prediction of ice nucleation from room temperature water
Crystal nucleation is one of the most fundamental processes in the physical sciences and almost always occurs heterogeneously with the aid of a nucleating substrate. No example of nucleation is more ubiquitous and impactful than the formation of ice, vital to fields as diverse as geology, biology, a...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9351478/ https://www.ncbi.nlm.nih.gov/pubmed/35878028 http://dx.doi.org/10.1073/pnas.2205347119 |
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author | Davies, Michael Benedict Fitzner, Martin Michaelides, Angelos |
author_facet | Davies, Michael Benedict Fitzner, Martin Michaelides, Angelos |
author_sort | Davies, Michael Benedict |
collection | PubMed |
description | Crystal nucleation is one of the most fundamental processes in the physical sciences and almost always occurs heterogeneously with the aid of a nucleating substrate. No example of nucleation is more ubiquitous and impactful than the formation of ice, vital to fields as diverse as geology, biology, aeronautics, and climate science. However, despite considerable effort, we still cannot predict a priori the efficacy of a nucleating agent. Here we utilize deep learning methods to accurately predict nucleation ability from images of room temperature liquid water—generated from molecular dynamics simulations—on a broad range of substrates. The resulting model, named IcePic, can rapidly and accurately infer nucleation ability, eliminating the requirement for either notoriously expensive simulations or direct experimental measurement. In an online poll, IcePic was found to significantly outperform humans in predicting the ice nucleating efficacy of materials. By analyzing the typical errors made by humans, as well as the application of reverse interpretation methods, physical insights into the role the water contact layer plays in ice nucleation have been obtained. Moving forward, we suggest that IcePic can be used as an easy, cheap, and rapid way to discern the nucleation ability of substrates, also with potential for learning other properties related to interfacial water. |
format | Online Article Text |
id | pubmed-9351478 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-93514782022-08-05 Accurate prediction of ice nucleation from room temperature water Davies, Michael Benedict Fitzner, Martin Michaelides, Angelos Proc Natl Acad Sci U S A Physical Sciences Crystal nucleation is one of the most fundamental processes in the physical sciences and almost always occurs heterogeneously with the aid of a nucleating substrate. No example of nucleation is more ubiquitous and impactful than the formation of ice, vital to fields as diverse as geology, biology, aeronautics, and climate science. However, despite considerable effort, we still cannot predict a priori the efficacy of a nucleating agent. Here we utilize deep learning methods to accurately predict nucleation ability from images of room temperature liquid water—generated from molecular dynamics simulations—on a broad range of substrates. The resulting model, named IcePic, can rapidly and accurately infer nucleation ability, eliminating the requirement for either notoriously expensive simulations or direct experimental measurement. In an online poll, IcePic was found to significantly outperform humans in predicting the ice nucleating efficacy of materials. By analyzing the typical errors made by humans, as well as the application of reverse interpretation methods, physical insights into the role the water contact layer plays in ice nucleation have been obtained. Moving forward, we suggest that IcePic can be used as an easy, cheap, and rapid way to discern the nucleation ability of substrates, also with potential for learning other properties related to interfacial water. National Academy of Sciences 2022-07-25 2022-08-02 /pmc/articles/PMC9351478/ /pubmed/35878028 http://dx.doi.org/10.1073/pnas.2205347119 Text en Copyright © 2022 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by/4.0/This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY) (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Physical Sciences Davies, Michael Benedict Fitzner, Martin Michaelides, Angelos Accurate prediction of ice nucleation from room temperature water |
title | Accurate prediction of ice nucleation from room temperature water |
title_full | Accurate prediction of ice nucleation from room temperature water |
title_fullStr | Accurate prediction of ice nucleation from room temperature water |
title_full_unstemmed | Accurate prediction of ice nucleation from room temperature water |
title_short | Accurate prediction of ice nucleation from room temperature water |
title_sort | accurate prediction of ice nucleation from room temperature water |
topic | Physical Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9351478/ https://www.ncbi.nlm.nih.gov/pubmed/35878028 http://dx.doi.org/10.1073/pnas.2205347119 |
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