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dc.contributor.authorSchran, Christoph
dc.contributor.authorThiemann, Fabian L
dc.contributor.authorRowe, Patrick
dc.contributor.authorMüller, Erich A
dc.contributor.authorMarsalek, Ondrej
dc.contributor.authorMichaelides, Angelos
dc.date.accessioned2021-09-16T23:31:12Z
dc.date.available2021-09-16T23:31:12Z
dc.date.issued2021-09-21
dc.identifier.issn0027-8424
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/328158
dc.description.abstract<jats:title>Significance</jats:title> <jats:p>Understanding complex materials, in particular those with solid–liquid interfaces, such as water on surfaces or under confinement, is a key challenge for technological and scientific progress. Although established simulation approaches have been able to provide important atomistic insight, ab initio techniques struggle with the required time and length scales, while force field methods can often be limited in terms of their accuracy. Here we show how these limitations can be overcome in a simple and automated machine learning procedure to provide accurate models of interactions at the ab initio level, as illustrated for a variety of complex aqueous systems. These developments open up the prospect of the straightforward exploration of many technologically relevant systems by molecular simulations.</jats:p>
dc.languageen
dc.publisherProceedings of the National Academy of Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleMachine learning potentials for complex aqueous systems made simple
dc.typeArticle
prism.endingPagee2110077118
prism.issueIdentifier38
prism.publicationDate2021
prism.publicationNameProceedings of the National Academy of Sciences
prism.startingPagee2110077118
prism.volume118
dc.identifier.doi10.17863/CAM.75613
dcterms.dateAccepted2021-07-27
rioxxterms.versionofrecord10.1073/pnas.2110077118
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2021-09-21
dc.contributor.orcidSchran, Christoph [0000-0003-4595-5073]
dc.contributor.orcidThiemann, Fabian L [0000-0003-2951-6740]
dc.contributor.orcidMüller, Erich A [0000-0002-1513-6686]
dc.contributor.orcidMarsalek, Ondrej [0000-0002-8624-8837]
dc.contributor.orcidMichaelides, Angelos [0000-0002-9169-169X]
dc.identifier.eissn1091-6490
rioxxterms.typeJournal Article/Review
cam.issuedOnline2021-09-13


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Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's licence is described as Attribution-NonCommercial-NoDerivatives 4.0 International