Dataset Open Access
Martinetto, Vincent;
Shah, Karan;
Cangi, Attila;
Pribram-Jones, Aurora
{
"license": "https://creativecommons.org/licenses/by/4.0/legalcode",
"@id": "https://doi.org/10.14278/rodare.2720",
"keywords": [
"density functional theory",
"machine learning"
],
"description": "<p>This data repository contains the datasets used in the paper "Inverting the Kohn-Sham equations with physics-informed machine learning". </p>\n\n<p>It contains the data generation scripts, datasets for the systems used in the paper (Single Well - 1D atom, Double Well - 1D diatomic molecule) and output potentials generated by the physics-informed machine learning models (physics-informed neural networks and Fourier neural operators).</p>",
"identifier": "https://doi.org/10.14278/rodare.2720",
"@context": "https://schema.org/",
"url": "https://rodare.hzdr.de/record/2720",
"@type": "Dataset",
"name": "Inverting the Kohn-Sham equations with physics-informed machine learning",
"creator": [
{
"@id": "https://orcid.org/0000-0001-6026-7397",
"name": "Martinetto, Vincent",
"affiliation": "Department of Chemistry and Biochemistry, University of California Merced, 5200 North Lake Rd., Merced, California 95343, USA",
"@type": "Person"
},
{
"@id": "https://orcid.org/0000-0002-5480-2880",
"name": "Shah, Karan",
"affiliation": "Center for Advanced Systems Understanding, 02826 G\u00f6rlitz, Germany/Helmholtz-Zentrum Dresden-Rossendorf, Bautzner Landstra\u00dfe 400, 01328 Dresden",
"@type": "Person"
},
{
"@id": "https://orcid.org/0000-0001-9162-262X",
"name": "Cangi, Attila",
"affiliation": "Center for Advanced Systems Understanding, 02826 G\u00f6rlitz, Germany/Helmholtz-Zentrum Dresden-Rossendorf, Bautzner Landstra\u00dfe 400, 01328 Dresden",
"@type": "Person"
},
{
"@id": "https://orcid.org/0000-0003-0244-1814",
"name": "Pribram-Jones, Aurora",
"affiliation": "Department of Chemistry and Biochemistry, University of California Merced, 5200 North Lake Rd., Merced, California 95343, USA",
"@type": "Person"
}
],
"datePublished": "2024-02-01",
"sameAs": [
"https://www.hzdr.de/publications/Publ-38725"
],
"distribution": [
{
"fileFormat": "zip",
"contentUrl": "https://rodare.hzdr.de/api/files/3603a26b-9e2c-4f6c-8132-71fb1194c54a/piml_ks_inversion.zip",
"@type": "DataDownload"
}
]
}
| All versions | This version | |
|---|---|---|
| Views | 821 | 821 |
| Downloads | 152 | 152 |
| Data volume | 23.6 GB | 23.6 GB |
| Unique views | 746 | 746 |
| Unique downloads | 148 | 148 |