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Data · dataset · 2026

Fine-Tuned Machine-Learning Potential for Accurate Description of Mn$_x$O$_y$H$_z$ Clusters on Cobalt Surfaces

Listed in RADAR and RADAR4Memory — shown once because both records carry DOI 10.35097/k9kebu3yb0cc8xcm

In this document, we present a fine-tuned version of the universal machine-learning po- tential (uMLP) CHGNet to accurately model MnxOyHz clusters on fcc-Co Surfaces.

Description

The pdf file explaining the procedure is divided in three sections: the first specifies the density functional theory (DFT) settings employed for the single-point (SP) calculations used for structures labeling, the second is related to the creation of the structure database and the third to the training procedure.

The structural database file used for the fine-tuning procedure is provided as both an ase .db and .json file.

Links

Where it is published

Catalogue records · 1

Topics

Inferred from text
Computational chemistry 77%
Provenance · 2 source records, 12 field assertions
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RADAR10.35097/k9kebu3yb0cc8xcm8 d agoJSON v1
RADAR4Memory10.35097/k9kebu3yb0cc8xcm8 d agoJSON v1
FieldAssertionExtractorEvidence
access_levelsource · radar service eu deconnector:radar_service_eu_de@1.0.0
concepts[field].anzsrc:field:340701enrichment · radar service eu detaxonomy-embedding@1.1.0title+keywords+description (77%)
concepts[field].local:field:chemistrymapping · radar service eu deconnector:radar_service_eu_de@1.0.0
concepts[field].local:field:chemistrymapping · radar4memory radar service euconnector:radar4memory_radar_service_eu@1.0.0
concepts[field].local:field:earth-environmentalmapping · radar service eu deconnector:radar_service_eu_de@1.0.0
concepts[field].local:field:earth-environmentalmapping · radar4memory radar service euconnector:radar4memory_radar_service_eu@1.0.0
concepts[method].local:method:dftmapping · radar4memory radar service euvocabulary-mapper@1.0.0keywords['DFT']
concepts[method].local:method:dftmapping · radar service eu devocabulary-mapper@1.0.0keywords['DFT']
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