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Achieving DFT accuracy with a machine-learning interatomic potential: Thermomechanics and defects in bcc ferromagnetic iron

cam.issuedOnline2018-01-30
dc.contributor.authorDragoni, D
dc.contributor.authorDaff, TD
dc.contributor.authorCsányi, G
dc.contributor.authorMarzari, N
dc.contributor.orcidDaff, Thomas [0000-0003-4837-4143]
dc.date.accessioned2018-12-15T00:30:22Z
dc.date.available2018-12-15T00:30:22Z
dc.date.issued2018
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.identifier.doi10.17863/CAM.34290
dc.identifier.eissn2475-9953
dc.identifier.issn2475-9953
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/286981
dc.language.isoeng
dc.publisherAmerican Physical Society (APS)
dc.publisher.urlhttp://dx.doi.org/10.1103/PhysRevMaterials.2.013808
dc.relation.replaces1810/274778
dc.relation.replaceshttps://www.repository.cam.ac.uk/handle/1810/274778
dc.rightsAll rights reserved
dc.subjectcond-mat.mtrl-sci
dc.subjectcond-mat.mtrl-sci
dc.titleAchieving DFT accuracy with a machine-learning interatomic potential: Thermomechanics and defects in bcc ferromagnetic iron
dc.typeArticle
dcterms.dateAccepted2017-11-16
prism.issueIdentifier1
prism.publicationDate2018
prism.publicationNamePhysical Review Materials
prism.volume2
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)
rioxxterms.licenseref.startdate2018-01-30
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeJournal Article/Review
rioxxterms.versionVoR
rioxxterms.versionofrecord10.1103/PhysRevMaterials.2.013808

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