Data · dataset · 2022
Temperature dependent properties of the aqueous electron
Listed in Materials Cloud Archive
The temperature-dependent properties of the aqueous electron have been extensively studied using mixed quantum-classical simulations in a wide range of thermodynamic conditions based on one-electron pseudopotentials.
Description
While the cavity model appears to explain most of the physical properties of the aqueous electron, only a non-cavity model has so far been successful in accounting for the temperature dependence of the absorption spectrum.
Here, we present an accurate and efficient description of the aqueous electron under various thermodynamic conditions by combining hybrid functional-based molecular dynamics, machine learning techniques, and multiple time-step methods. Our advanced simulations accurately describe the temperature dependence of the absorption maximum in the presence of cavity formation. Specifically, our work reveals that the red shift of the absorption maximum results from an increasing gyration radius with temperature, rather than from global density variations as previously suggested.
Links
Where it is published
- Materials Cloud Archive record archive.materialscloud.org/records/ynqy2-x7b05 ↗
landing page · from archive materialscloud org
- DOI doi.org/10.24435/materialscloud:cv-0v ↗
DOI / persistent id · from archive materialscloud org
Catalogue records · 1
- InvenioRDM API archive.materialscloud.org/api/records/ynqy2-x7b05 ↗
metadata API · from archive materialscloud org
Topics
- Stated by source
- ab initio molecular dynamics · Aqueous Electron · Hybrid functional · Machine-learning · MARVEL
- From keywords
- Computer Science & AI · Life Sciences · Machine learning
Provenance · 1 source records, 14 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Materials Cloud Archive | ynqy2-x7b05 | 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[field].local:field:life-sciences | mapping · archive materialscloud org | connector:archive_materialscloud_org@1.0.0 | |
| concepts[topic].invenio_subject:archive_materialscloud_org:ab-initio-molecular-dynamics | source · archive materialscloud org | connector:archive_materialscloud_org@1.0.0 | |
| concepts[topic].invenio_subject:archive_materialscloud_org:aqueous-electron | source · archive materialscloud org | connector:archive_materialscloud_org@1.0.0 | |
| concepts[topic].invenio_subject:archive_materialscloud_org:hybrid-functional | 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 |