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dc.contributor.authorBartók, Albert Pen
dc.contributor.authorCsányi, Gáboren
dc.date.accessioned2015-05-27T11:40:32Z
dc.date.available2015-05-27T11:40:32Z
dc.date.issued2015-04-27en
dc.identifier.citationQuantum Chemistry Volume 115, Issue 16, pages 1051–1057, August 15, 2015. DOI: 10.1002/qua.24927en
dc.identifier.issn0020-7608
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/248022
dc.description.abstractWe present a swift walk-through of our recent work that uses machine learning to t interatomic potentials based on quantum mechanical data. We describe our Gaussian Approximation Potentials (GAP) framework, discuss a variety of descriptors, how to train the model on total energies and derivatives and the simultaneous use of multiple models of di erent complexity. We also show a small example using QUIP, the software sandbox implementation of GAP that is available for non-commercial use. 1
dc.description.sponsorshipA.P.B. is supported by a Leverhulme Early Career Fellowship and the Isaac Newton Trust. We would like to thank our referees for their comments during the revision process.
dc.languageEnglishen
dc.language.isoenen
dc.publisherWiley
dc.subjectinteratomic potentialsen
dc.subjectmachine learningen
dc.subjectGaussian processen
dc.subjectab initioen
dc.subjectatomic environmentsen
dc.titleGaussian Approximation Potentials: a brief tutorial introductionen
dc.typeArticle
dc.description.versionThis is the author accepted manuscript. The final version is available via Wiley at http://onlinelibrary.wiley.com/doi/10.1002/qua.24927/abstract.en
prism.publicationDate2015en
prism.publicationNameInternational Journal of Quantum Chemistryen
prism.startingPage1051
prism.volume115en
rioxxterms.versionofrecord10.1002/qua.24927en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2015-04-27en
dc.identifier.eissn1097-461X
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idEPSRC (VIA UNIVERSITY OF OXFORD) (EP/L014742/1)
rioxxterms.freetoread.startdate2016-04-27


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