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dc.contributor.authorvon Wolff, Niklas-
dc.coverage.spatialES Barcelona Barcelona-
dc.date.accessioned2021-11-17T10:00:09Z-
dc.date.available2023-05-01T13:51:25Z-
dc.date.created2021-11-17T11:00:07.424+01:00-
dc.date.issued2021-11-17T11:00:07.424+01:00-
dc.identifier.urihttps://iochem-bd.bsc.es/browse/handle/100/200368-
dc.descriptionEster 98-
dc.publisherBarcelona Supercomputing Center-
dc.relationDOI: 10.1039/D3DD00029J*
dc.relation.ispartofhttp://dx.doi.org/10.19061/iochem-bd-6-118-
dc.relation.urihttp://dx.doi.org/10.1039/D3DD00029J*
dc.rightsCC BY 4.0 (c) Barcelona Supercomputing Center, 2021-
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/-
dc.titleE098-
dc.typedataset-
dc.date.updated2021-11-17T10:00:09Z-
cml.program.nameGaussianen
cml.program.version16en
cml.program.otherES64L-G16RevC.01en
cml.methodRM06Len
cml.basissetDEF2SVP DEF2SVP/W06en
cml.multiplicity1en
cml.spintypeRestricteden
cml.shelltypeCloseden
cml.charge0en
cml.energy.value-582.406799740en
cml.energy.unitsEhen
cml.formula.genericC11H22O2en
cml.calculationtypeGeometry optimization Minimumen
cml.hassolventtrueen
cml.hasvibrationalfrequenciestrueen
cml.numberofjobs2en
cml.hasmolecularorbitalsfalseen
Appears in Collections:Predicting_Ruthenium_Catalysed_Hydrogenation_Machine_Learning - DOI: 10.19061/iochem-bd-6-118



Please use this identifier to cite or link to this item: https://iochem-bd.bsc.es/browse/handle/100/200368

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