Data · dataset · 2025
Dataset of self-consistent Hubbard parameters for Ni, Mn and Fe from linear-response
Listed in Materials Cloud Archive
Density-functional theory with extended Hubbard functionals (DFT+U+V) provides a robust framework to accurately describe complex materials containing transition-metal or rare-earth elements.
Description
It does so by mitigating self-interaction errors inherent to semi-local functionals which are particularly pronounced in systems with partially-filled d and f electronic states. However, achieving accuracy in this approach hinges upon the accurate determination of the on-site U and inter-site V Hubbard parameters.
In practice, these are obtained either by semi-empirical tuning, requiring prior knowledge, or, more correctly, by using predictive but expensive first-principles calculations. This archive entry contains Hubbard parameters, occupation matrices and other data calculated for 28 materials and covers all steps in a self-consistent procedure where, at each step new Hubbard parameters are obtained via linear-response, a process that is repeated until the parameters no longer change.
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The primary purpose of this dataset is to support the development and validation of machine learning models that can be used to predict Hubbard parameters, sidestepping the need for expensive ab-initio density functional perturbation theory calculations.
Links
Where it is published
- Materials Cloud Archive record archive.materialscloud.org/records/3tcgv-42e20 ↗
landing page · from archive materialscloud org
- DOI doi.org/10.24435/materialscloud:vb-g4 ↗
DOI / persistent id · from archive materialscloud org
Catalogue records · 1
- InvenioRDM API archive.materialscloud.org/api/records/3tcgv-42e20 ↗
metadata API · from archive materialscloud org
Topics
- Stated by source
- density functional theory · Hubbard corrections · Machine-learning · MARVEL
- From keywords
- Computer Science & AI · Density functional theory · Machine learning
Provenance · 1 source records, 13 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Materials Cloud Archive | 3tcgv-42e20 | 2 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · archive materialscloud org | connector:archive_materialscloud_org@1.0.0 | |
| concepts[field].anzsrc:group:4611 | mapping · archive materialscloud org | vocabulary-mapper@1.0.0 | keywords['machine learning'] |
| concepts[field].local:field:computer-science-ai | mapping · archive materialscloud org | connector:archive_materialscloud_org@1.0.0 | |
| concepts[method].local:method:dft | mapping · archive materialscloud org | vocabulary-mapper@1.0.0 | keywords['density-functional theory'] |
| concepts[topic].invenio_subject:archive_materialscloud_org:density-functional-theory | source · archive materialscloud org | connector:archive_materialscloud_org@1.0.0 | |
| concepts[topic].invenio_subject:archive_materialscloud_org:hubbard-corrections | source · archive materialscloud org | connector:archive_materialscloud_org@1.0.0 | |
| concepts[topic].invenio_subject:archive_materialscloud_org:machine-learning | source · archive materialscloud org | connector:archive_materialscloud_org@1.0.0 | |
| concepts[topic].invenio_subject:archive_materialscloud_org:marvel | source · archive materialscloud org | connector:archive_materialscloud_org@1.0.0 | |
| description | source · archive materialscloud org | connector:archive_materialscloud_org@1.0.0 | /metadata/description |
| license | source · archive materialscloud org | connector:archive_materialscloud_org@1.0.0 | |
| publication_date | source · archive materialscloud org | connector:archive_materialscloud_org@1.0.0 | |
| title | source · archive materialscloud org | connector:archive_materialscloud_org@1.0.0 | /metadata/title |
| updated_date | source · archive materialscloud org | connector:archive_materialscloud_org@1.0.0 |