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dc.contributor.authorSivaraman, G
dc.contributor.authorKrishnamoorthy, AN
dc.contributor.authorBaur, M
dc.contributor.authorHolm, C
dc.contributor.authorStan, M
dc.contributor.authorCsányi, G
dc.contributor.authorBenmore, C
dc.contributor.authorVázquez-Mayagoitia, Á
dc.date.accessioned2020-08-03T23:31:14Z
dc.date.available2020-08-03T23:31:14Z
dc.date.issued2020
dc.identifier.issn2057-3960
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/308724
dc.description.abstract<jats:title>Abstract</jats:title><jats:p>We propose an active learning scheme for automatically sampling a minimum number of uncorrelated configurations for fitting the Gaussian Approximation Potential (GAP). Our active learning scheme consists of an unsupervised machine learning (ML) scheme coupled with a Bayesian optimization technique that evaluates the GAP model. We apply this scheme to a Hafnium dioxide (HfO<jats:sub>2</jats:sub>) dataset generated from a “melt-quench” ab initio molecular dynamics (AIMD) protocol. Our results show that the active learning scheme, with no prior knowledge of the dataset, is able to extract a configuration that reaches the required energy fit tolerance. Further, molecular dynamics (MD) simulations performed using this active learned GAP model on 6144 atom systems of amorphous and liquid state elucidate the structural properties of HfO<jats:sub>2</jats:sub> with near ab initio precision and quench rates (i.e., 1.0 K/ps) not accessible via AIMD. The melt and amorphous X-ray structural factors generated from our simulation are in good agreement with experiment. In addition, the calculated diffusion constants are in good agreement with previous ab initio studies.</jats:p>
dc.publisherSpringer Science and Business Media LLC
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleMachine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide
dc.typeArticle
prism.issueIdentifier1
prism.publicationDate2020
prism.publicationNamenpj Computational Materials
prism.volume6
dc.identifier.doi10.17863/CAM.55813
dcterms.dateAccepted2020-06-22
rioxxterms.versionofrecord10.1038/s41524-020-00367-7
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2020-12-01
dc.contributor.orcidSivaraman, G [0000-0001-9056-9855]
dc.contributor.orcidBaur, M [0000-0002-4589-3813]
dc.contributor.orcidVázquez-Mayagoitia, Á [0000-0002-1415-6300]
dc.identifier.eissn2057-3960
rioxxterms.typeJournal Article/Review
cam.issuedOnline2020-07-23


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