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dc.contributor.authorRowe, P
dc.contributor.authorCsányi, G
dc.contributor.authorAlfè, D
dc.contributor.authorMichaelides, Angelos
dc.date.accessioned2018-12-15T00:30:20Z
dc.date.available2018-12-15T00:30:20Z
dc.date.issued2018-02-05
dc.identifier.issn2469-9950
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/286980
dc.description.abstractWe present an accurate interatomic potential for graphene, constructed using the Gaussian Approximation Potential (GAP) machine learning methodology. This GAP model obtains a faithful representation of a density functional theory (DFT) potential energy surface, facilitating highly accurate (approaching the accuracy of ab initio methods) molecular dynamics simulations. This is achieved at a computational cost which is orders of magnitude lower than that of comparable calculations which directly invoke electronic structure methods. We evaluate the accuracy of our machine learning model alongside that of a number of popular empirical and bond-order potentials, using both experimental and ab initio data as references. We find that whilst significant discrepancies exist between the empirical interatomic potentials and the reference data - and amongst the empirical potentials themselves - the machine learning model introduced here provides exemplary performance in all of the tested areas. The calculated properties include: graphene phonon dispersion curves at 0 K (which we predict with sub-meV accuracy), phonon spectra at finite temperature, in-plane thermal expansion up to 2500 K as compared to NPT ab initio molecular dynamics simulations and a comparison of the thermally induced dispersion of graphene Raman bands to experimental observations. We have made our potential freely available online at [http://www.libatoms.org].
dc.publisherAmerican Physical Society (APS)
dc.titleDevelopment of a machine learning potential for graphene
dc.typeArticle
prism.issueIdentifier5
prism.publicationDate2018
prism.publicationNamePhysical Review B
prism.volume97
dc.identifier.doi10.17863/CAM.34289
dcterms.dateAccepted2018-01-12
rioxxterms.versionofrecord10.1103/PhysRevB.97.054303
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2018-02-05
dc.contributor.orcidMichaelides, Angelos [0000-0002-9169-169X]
dc.identifier.eissn2469-9969
dc.publisher.urlhttp://dx.doi.org/10.1103/PhysRevB.97.054303
rioxxterms.typeJournal Article/Review
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/P022596/1)
cam.issuedOnline2018-02-05
rioxxterms.freetoread.startdate2019-02-05


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