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dc.contributor.authorSivaraman, Gen
dc.contributor.authorKrishnamoorthy, ANen
dc.contributor.authorBaur, Men
dc.contributor.authorHolm, Cen
dc.contributor.authorStan, Men
dc.contributor.authorCsányi, Gen
dc.contributor.authorBenmore, Cen
dc.contributor.authorVázquez-Mayagoitia, Áen
dc.date.accessioned2020-08-03T23:31:14Z
dc.date.available2020-08-03T23:31:14Z
dc.date.issued2020-12-01en
dc.identifier.issn2057-3960
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/308724
dc.description.abstract© 2020, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply. 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 (HfO2) 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 HfO2 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.
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 dioxideen
dc.typeArticle
prism.issueIdentifier1en
prism.publicationDate2020en
prism.publicationNamenpj Computational Materialsen
prism.volume6en
dc.identifier.doi10.17863/CAM.55813
dcterms.dateAccepted2020-06-22en
rioxxterms.versionofrecord10.1038/s41524-020-00367-7en
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2020-12-01en
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/Reviewen


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