Data · dataset · 2024
Spectral operator representations
Listed in Materials Cloud Archive
Materials are often represented in machine learning applications by (chemical-)geometric descriptions of their atomic structure.
Description
In this work, we propose an alternative framework for representing materials using descriptions of their electronic structure called Spectral Operator Representations (SOREPs). This record contains the code and data used to study carbon nanotubes (CNTs), barium titanate polymorphs, and the accelerated screening of transparent conducting materials with SOREPs.
A data set for each application is provided: p z tight binding band structures for the three CNT configurations studied; the structures, band dispersions, and SOREP features of 127 BaTiO₃ polymorphs; and the SOREP features and ML targets for the MC3D materials considered in the accelerated screening. Additionally, code including patch files for Quantum ESPRESSO, the "sorep" python package, and the set of scripts used to prepare these data, train ML models, and plot results is provided.
Links
Where it is published
- Materials Cloud Archive record archive.materialscloud.org/records/m06a9-aqx95 ↗
landing page · from archive materialscloud org
- DOI doi.org/10.24435/materialscloud:vm-5n ↗
DOI / persistent id · from archive materialscloud org
Catalogue records · 1
- InvenioRDM API archive.materialscloud.org/api/records/m06a9-aqx95 ↗
metadata API · from archive materialscloud org
Topics
- Stated by source
- electronic bands · electronic structure · EPFL · Machine-learning · MARVEL
- From keywords
- Computer Science & AI · Machine learning
Provenance · 1 source records, 13 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Materials Cloud Archive | m06a9-aqx95 | 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[topic].invenio_subject:archive_materialscloud_org:electronic-bands | source · archive materialscloud org | connector:archive_materialscloud_org@1.0.0 | |
| concepts[topic].invenio_subject:archive_materialscloud_org:electronic-structure | source · archive materialscloud org | connector:archive_materialscloud_org@1.0.0 | |
| concepts[topic].invenio_subject:archive_materialscloud_org:epfl | 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 |