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Manuscript title: Statistical learning predicts the thermochemistry of adsorbed species on transition metals beyond the d-band model

Journal: Nat. Commun.

DOI: 10.1038/s41467-019-12709-1

Full metadata record
DC FieldValueLanguage
dc.contributor.authorGarcía Muelas, Rodrigo-
dc.date.accessioned2019-02-12T11:23:48Z-
dc.date.available2019-10-17T08:01:16Z-
dc.date.created2019-02-12T12:23:46.764+01:00-
dc.date.issued2019-02-12T12:23:46.764+01:00-
dc.identifier.urihttps://iochem-bd.iciq.es/browse/handle/100/18140-
dc.descriptionValidation set for Alloy: ss-RuZn-1401. Legend on Nat. Commun. 2018, 9, 526.-
dc.publisherInstitute of Chemical Research of Catalonia-
dc.relationOriginal title: Statistical learning predicts the thermochemistry of adsorbed species on transition metals beyond the d-band model DOI: 10.1038/s41467-019-12709-1 Journal: Nat. Commun.*
dc.relation.urihttp://dx.doi.org/10.1038/s41467-019-12709-1*
dc.rightsCC BY 4.0 (c) Institute of Chemical Research of Catalonia, 2019-
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/-
dc.subjectBilinear model-
dc.subjectAlcohols-
dc.subjectTransition Metals-
dc.subjectPrincipal Component Regression-
dc.subjectd-band center-
dc.subjectReduction potential-
dc.subjectBilinear regression-
dc.subjectPrincipal Component Analysis-
dc.subjectSugar alcohols-
dc.subjectThermochemistry-
dc.subjectBiomass upgrade-
dc.titless-RuZn-1401-
dc.typedataset-
dc.date.updated2019-02-12T11:23:48Z-
cml.program.namevaspen
cml.program.version5.4.4en
cml.program.other18Apr17-6-g9f103f2a35 (build Jun 30 2017 19:19:18) complexen
cml.methodDFTen
cml.shelltypeClosed shellen
cml.energy.value-345.97562257en
cml.energy.unitseVen
cml.formula.genericCH4Ru36Zn12en
cml.calculationtypeGeometry optimizationen
cml.hassolventfalseen
cml.hasvibrationalfrequenciesfalseen
cml.numberofjobs1en
cml.hasmolecularorbitalsfalseen
Appears in Collections:Statistical learning goes beyond the d-band model providing the thermochemistry of adsorbates on transition metals - DOI: 10.19061/iochem-bd-1-43



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