Show simple item record

dc.contributor.authorBartók, Albert P
dc.contributor.authorDe, Sandip
dc.contributor.authorPoelking, Carl
dc.contributor.authorBernstein, Noam
dc.contributor.authorKermode, James R
dc.contributor.authorCsányi, Gábor
dc.contributor.authorCeriotti, Michele
dc.date.accessioned2018-04-12T10:39:26Z
dc.date.available2018-04-12T10:39:26Z
dc.date.issued2017-12
dc.identifier.issn2375-2548
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/274800
dc.description.abstractDetermining the stability of molecules and condensed phases is the cornerstone of atomistic modeling, underpinning our understanding of chemical and materials properties and transformations. We show that a machine-learning model, based on a local description of chemical environments and Bayesian statistical learning, provides a unified framework to predict atomic-scale properties. It captures the quantum mechanical effects governing the complex surface reconstructions of silicon, predicts the stability of different classes of molecules with chemical accuracy, and distinguishes active and inactive protein ligands with more than 99% reliability. The universality and the systematic nature of our framework provide new insight into the potential energy surface of materials and molecules.
dc.format.mediumElectronic-eCollection
dc.languageeng
dc.publisherAmerican Association for the Advancement of Science (AAAS)
dc.rightsAttribution-NonCommercial 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.titleMachine learning unifies the modeling of materials and molecules.
dc.typeArticle
prism.issueIdentifier12
prism.publicationDate2017
prism.publicationNameSci Adv
prism.startingPagee1701816
prism.volume3
dc.identifier.doi10.17863/CAM.21944
dcterms.dateAccepted2017-11-14
rioxxterms.versionofrecord10.1126/sciadv.1701816
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2017-12-13
dc.contributor.orcidBartók, Albert P [0000-0002-4347-8819]
dc.contributor.orcidDe, Sandip [0000-0001-8434-3497]
dc.contributor.orcidKermode, James R [0000-0001-6755-6271]
dc.contributor.orcidCsányi, Gábor [0000-0002-8180-2034]
dc.contributor.orcidCeriotti, Michele [0000-0003-2571-2832]
dc.identifier.eissn2375-2548
dc.publisher.urlhttp://dx.doi.org/10.1126/sciadv.1701816
rioxxterms.typeJournal Article/Review
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/K014560/1)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/L014742/1)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/P022596/1)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/J010847/1)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/J022012/1)
pubs.funder-project-idEuropean Commission Horizon 2020 (H2020) Research Infrastructures (RI) (676580)
cam.issuedOnline2017-12-13
cam.orpheus.successThu Jan 30 12:59:55 GMT 2020 - The item has an open VoR version.
rioxxterms.freetoread.startdate2100-01-01


Files in this item

Thumbnail
Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record

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