Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Davies, Michael Benedict, Fitzner, Martin, Michaelides, Angelos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2022
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
_version_ 1784762452429242368
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
work_keys_str_mv AT daviesmichaelbenedict accuratepredictionoficenucleationfromroomtemperaturewater
AT fitznermartin accuratepredictionoficenucleationfromroomtemperaturewater
AT michaelidesangelos accuratepredictionoficenucleationfromroomtemperaturewater