Research data supporting "An Accurate and Transferable Machine Learning Potential for Carbon"

dc.contributor.author	Csanyi, Gabor
dc.date.accessioned	2022-03-08T16:04:43Z
dc.date.available	2022-03-08T16:04:43Z
dc.identifier.uri	https://www.repository.cam.ac.uk/handle/1810/334766
dc.description	This is a machine learning interatomic potential for carbon, using the GAP framework.
dc.format	GAP, QUIP (http:/www.github.com/libAtoms/GAP)
dc.rights	Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
dc.rights.uri	https://creativecommons.org/licenses/by-nc/4.0/
dc.subject	interatomic potential
dc.subject	carbon
dc.subject	molecular dynamics
dc.subject	machine learning
dc.title	Research data supporting "An Accurate and Transferable Machine Learning Potential for Carbon"
dc.type	Dataset
dc.publisher.department	Department of Engineering
dc.date.updated	2022-03-04T13:53:33Z
dc.identifier.doi	10.17863/CAM.82086
rioxxterms.licenseref.uri	https://creativecommons.org/licenses/by-nc/4.0/
dcterms.format	zip
dc.contributor.orcid	Csanyi, Gabor [0000-0002-8180-2034]
rioxxterms.type	Other
datacite.issupplementedby.doi	10.17863/CAM.84096
datacite.issupplementto.url	https://www.repository.cam.ac.uk/handle/1810/315375
cam.depositDate	2022-03-04
pubs.licence-identifier	apollo-deposit-licence-2-1
pubs.licence-display-name	Apollo Repository Deposit Licence Agreement
datacite.isderivedfrom.doi	10.1063/5.0005084

Except where otherwise noted, this item's licence is described as Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)