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dc.contributor.authorBernstein, Noamen
dc.contributor.authorCsanyi, Gaboren
dc.contributor.authorDeringer, Volkeren
dc.date.accessioned2019-10-11T10:39:07Z
dc.date.available2019-10-11T10:39:07Z
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/297743
dc.descriptionThis dataset supports our work on Gaussian Approximation Potential driven random structure searching (GAP-RSS) models for exploring and fitting potential-energy surfaces of materials. It provides, in separate tar archives, an implementation of the methodology and the final GAP-RSS models as reported in the associated publication.en
dc.formatThe GAP models are provided as parameter files to be used with the associated computer code, which is freely available for non-commercial research (http://www.libatoms.org/gap/gap_download.html).en
dc.rightsAttribution 4.0 Internationalen
dc.rightsAttribution 4.0 Internationalen
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectdensity functional theoryen
dc.subjectmachine learningen
dc.titleResearch data supporting "De novo exploration and self-guided learning of potential-energy surfaces"en
dc.typeDataset
dc.identifier.doi10.17863/CAM.43407
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/en
dcterms.formattar.bz2en
dc.contributor.orcidCsanyi, Gabor [0000-0002-8180-2034]
dc.contributor.orcidDeringer, Volker [0000-0001-6873-0278]
rioxxterms.typeOtheren
datacite.issupplementto.doi10.1038/s41524-019-0236-6en
datacite.issupplementto.urlhttps://www.repository.cam.ac.uk/handle/1810/296772


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