Machine learning potentials for complex aqueous systems made simple
dc.contributor.author | Schran, Christoph | |
dc.contributor.author | Thiemann, Fabian L | |
dc.contributor.author | Rowe, Patrick | |
dc.contributor.author | Müller, Erich A | |
dc.contributor.author | Marsalek, Ondrej | |
dc.contributor.author | Michaelides, Angelos | |
dc.date.accessioned | 2021-09-16T23:31:12Z | |
dc.date.available | 2021-09-16T23:31:12Z | |
dc.date.issued | 2021-09-21 | |
dc.identifier.issn | 0027-8424 | |
dc.identifier.uri | https://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.language | en | |
dc.publisher | Proceedings of the National Academy of Sciences | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.title | Machine learning potentials for complex aqueous systems made simple | |
dc.type | Article | |
prism.endingPage | e2110077118 | |
prism.issueIdentifier | 38 | |
prism.publicationDate | 2021 | |
prism.publicationName | Proceedings of the National Academy of Sciences | |
prism.startingPage | e2110077118 | |
prism.volume | 118 | |
dc.identifier.doi | 10.17863/CAM.75613 | |
dcterms.dateAccepted | 2021-07-27 | |
rioxxterms.versionofrecord | 10.1073/pnas.2110077118 | |
rioxxterms.version | VoR | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.licenseref.startdate | 2021-09-21 | |
dc.contributor.orcid | Schran, Christoph [0000-0003-4595-5073] | |
dc.contributor.orcid | Thiemann, Fabian L [0000-0003-2951-6740] | |
dc.contributor.orcid | Müller, Erich A [0000-0002-1513-6686] | |
dc.contributor.orcid | Marsalek, Ondrej [0000-0002-8624-8837] | |
dc.contributor.orcid | Michaelides, Angelos [0000-0002-9169-169X] | |
dc.identifier.eissn | 1091-6490 | |
rioxxterms.type | Journal Article/Review | |
cam.issuedOnline | 2021-09-13 |
Files in this item
This item appears in the following Collection(s)
-
Cambridge University Research Outputs
Research outputs of the University of Cambridge