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dc.contributor.authorDragoni, D
dc.contributor.authorDaff, Thomas
dc.contributor.authorCsányi, G
dc.contributor.authorMarzari, N
dc.date.accessioned2018-12-15T00:30:22Z
dc.date.available2018-12-15T00:30:22Z
dc.date.issued2018-01-30
dc.identifier.issn2475-9953
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/286981
dc.description.abstractWe show that the Gaussian Approximation Potential machine learning framework can describe complex magnetic potential energy surfaces, taking ferromagnetic iron as a paradigmatic challenging case. The training database includes total energies, forces, and stresses obtained from density-functional theory in the generalized-gradient approximation, and comprises approximately 150,000 local atomic environments, ranging from pristine and defected bulk configurations to surfaces and generalized stacking faults with different crystallographic orientations. We find the structural, vibrational and thermodynamic properties of the GAP model to be in excellent agreement with those obtained directly from first-principles electronic-structure calculations. There is good transferability to quantities, such as Peierls energy barriers, which are determined to a large extent by atomic configurations that were not part of the training set. We observe the benefit and the need of using highly converged electronic-structure calculations to sample a target potential energy surface. The end result is a systematically improvable potential that can achieve the same accuracy of density-functional theory calculations, but at a fraction of the computational cost.
dc.publisherAmerican Physical Society (APS)
dc.relation.replaces1810/274778
dc.relation.replaceshttps://www.repository.cam.ac.uk/handle/1810/274778
dc.rightsAll rights reserved
dc.titleAchieving DFT accuracy with a machine-learning interatomic potential: Thermomechanics and defects in bcc ferromagnetic iron
dc.typeArticle
prism.issueIdentifier1
prism.publicationDate2018
prism.publicationNamePhysical Review Materials
prism.volume2
dc.identifier.doi10.17863/CAM.34290
dcterms.dateAccepted2017-11-16
rioxxterms.versionofrecord10.1103/PhysRevMaterials.2.013808
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2018-01-30
dc.contributor.orcidDaff, Thomas [0000-0003-4837-4143]
dc.identifier.eissn2475-9953
dc.publisher.urlhttp://dx.doi.org/10.1103/PhysRevMaterials.2.013808
rioxxterms.typeJournal Article/Review
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/L014742/1)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/K014560/1)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/P022596/1)
cam.issuedOnline2018-01-30
rioxxterms.freetoread.startdate2019-01-30


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