Dataset Open Access
Kumar, Sandeep;
Tahmasbi, Hossein;
Ramakrishna, Kushal;
Lokamani, Mani;
Nikolov, Svetoslav;
Tranchida, Julien;
Wood, Mitchell A.;
Cangi, Attila
{
"creator": [
{
"@id": "https://orcid.org/0000-0002-6398-0427",
"@type": "Person",
"affiliation": "Center for Advanced Systems Understanding (CASUS), Helmholtz-Zentrum Dresden-Rossendorf (HZDR), G\u00f6rlitz, Germany",
"name": "Kumar, Sandeep"
},
{
"@id": "https://orcid.org/0000-0002-3072-8217",
"@type": "Person",
"affiliation": "Center for Advanced Systems Understanding (CASUS), Helmholtz-Zentrum Dresden-Rossendorf (HZDR), G\u00f6rlitz, Germany",
"name": "Tahmasbi, Hossein"
},
{
"@id": "https://orcid.org/0000-0003-4211-2484",
"@type": "Person",
"affiliation": "Center for Advanced Systems Understanding (CASUS), Helmholtz-Zentrum Dresden-Rossendorf (HZDR), G\u00f6rlitz, Germany",
"name": "Ramakrishna, Kushal"
},
{
"@id": "https://orcid.org/0000-0001-8679-5905",
"@type": "Person",
"affiliation": "Center for AdHelmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany",
"name": "Lokamani, Mani"
},
{
"@type": "Person",
"affiliation": "Computational Multiscale Department, Sandia National Laboratories, 87185 Albuquerque, NM, United States",
"name": "Nikolov, Svetoslav"
},
{
"@type": "Person",
"affiliation": "CEA, DES, IRESNE, DEC, SESC, LM2C, F-13108 Saint-Paul-Lez-Durance, France",
"name": "Tranchida, Julien"
},
{
"@type": "Person",
"affiliation": "Computational Multiscale Department, Sandia National Laboratories, 87185 Albuquerque, NM, United States",
"name": "Wood, Mitchell A."
},
{
"@id": "https://orcid.org/0000-0001-9162-262X",
"@type": "Person",
"affiliation": "Center for Advanced Systems Understanding (CASUS), Helmholtz-Zentrum Dresden-Rossendorf (HZDR), G\u00f6rlitz, Germany",
"name": "Cangi, Attila"
}
],
"url": "https://rodare.hzdr.de/record/2369",
"@id": "https://doi.org/10.14278/rodare.2369",
"@type": "Dataset",
"description": "<p>Here, we provide FitSNAP and DAKOTA input scripts and DFT-MD training data sets used for the generation of transferable SNAP ML-IAP for aluminum.</p>",
"sameAs": [
"https://www.hzdr.de/publications/Publ-37282"
],
"name": "Training scripts and input data sets: Transferable Interatomic Potential for Aluminum from Ambient Conditions to Warm Dense Matter",
"license": "",
"distribution": [
{
"@type": "DataDownload",
"fileFormat": "xz",
"contentUrl": "https://rodare.hzdr.de/api/files/907a0a20-10dd-464e-a4e3-aba99357eab7/FitSNAP.tar.xz"
}
],
"keywords": [
"Machine Learning Potential",
"Warm Dense Matter"
],
"@context": "https://schema.org/",
"datePublished": "2023-07-18",
"identifier": "https://doi.org/10.14278/rodare.2369"
}
| All versions | This version | |
|---|---|---|
| Views | 1,020 | 1,020 |
| Downloads | 131 | 131 |
| Data volume | 7.4 GB | 7.4 GB |
| Unique views | 938 | 938 |
| Unique downloads | 119 | 119 |