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dc.contributor.authorMocanu, FC
dc.contributor.authorKonstantinou, Konstantinos
dc.contributor.authorLee, Tae Hoon
dc.contributor.authorBernstein, Noam
dc.contributor.authorDeringer, Volker
dc.contributor.authorCsanyi, Gabor
dc.contributor.authorElliott, Stephen
dc.date.accessioned2018-10-03T04:46:07Z
dc.date.available2018-10-03T04:46:07Z
dc.date.issued2018-09-27
dc.identifier.issn1520-6106
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/283117
dc.description.abstractThe phase-change material, Ge2Sb2Te5, is the canonical material ingredient for next-generation storage-class memory devices used in novel computing architectures, but fundamental questions remain regarding its atomic structure and physico-chemical properties. Here, we introduce a machine-learning (ML)-based interatomic potential that enables large-scale atomistic simulations of liquid, amorphous, and crystalline Ge2Sb2Te5 with an unprecedented combination of speed and density-functional theory (DFT) level of accuracy. Two applications exemplify the usefulness of such an ML-driven approach: we generate a 7,200-atom structural model, hitherto inaccessible with DFT simulations, that affords new insight into the medium-range structural order; and we create an ensemble of uncorrelated, smaller structures, for studies of their chemical bonding with statistical significance. Our work opens the way for new atomistic insights into the fascinating and chemically complex class of phase-change materials that are used in real non-volatile memory devices.
dc.publisherAmerican Chemical Society (ACS)
dc.titleModeling the Phase-Change Memory Material, Ge2Sb2Te5, with a Machine-Learned Interatomic Potential
dc.typeArticle
prism.endingPage9006
prism.issueIdentifier38
prism.publicationNameThe Journal of Physical Chemistry Part B
prism.startingPage8998
prism.volume122
dc.identifier.doi10.17863/CAM.30478
dcterms.dateAccepted2018-09-01
rioxxterms.versionofrecord10.1021/acs.jpcb.8b06476
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2018-09-01
dc.contributor.orcidMocanu, Felix-Cosmin [0000-0001-6649-3029]
dc.contributor.orcidKonstantinou, Konstantinos [0000-0003-1291-817X]
dc.contributor.orcidDeringer, Volker [0000-0001-6873-0278]
dc.contributor.orcidCsanyi, Gabor [0000-0002-8180-2034]
dc.identifier.eissn1520-5207
rioxxterms.typeJournal Article/Review
pubs.funder-project-idEPSRC (1502879)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/K014560/1)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/P022596/1)
pubs.funder-project-idIsaac Newton Trust (1624(n))
pubs.funder-project-idIsaac Newton Trust (17.08(c))
pubs.funder-project-idLeverhulme Trust (ECF-2017-278)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/L015552/1)
cam.issuedOnline2018-09-01
datacite.issupplementedby.doi10.17863/CAM.26412
rioxxterms.freetoread.startdate2019-09-01


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