Dataset Open Access
{
"name": "Boron data set for machine learning applications",
"description": "<p><strong>Boron data set for machine learning applications</strong></p>\n\n<p>This dataset contains DFT inputs, outputs, LDOS data and bispectrum descriptor vectors for an α-rhombohedral boron cell of 144 atoms at room temperature and ambient mass density. All simulations have been performed at an LDOS converged k-grid of 4x4x4 k-points.</p>\n\n<p>This dataset contains one .zip file for each of its five type of data (bispectrum descriptors, LDOS, DFT inputs, DFT outputs and trained models).</p>\n\n<p><em>Authors:</em></p>\n\n<p>- Fiedler, Lenz (HZDR / CASUS)<br>\n- Cangi, Attila (HZDR / CASUS)</p>\n\n<p>Affiliations<em>:</em></p>\n\n<p>HZDR - Helmholtz-Zentrum Dresden-Rossendorf<br>\nCASUS - Center for Advanced Systems Understanding</p>\n\n<p><em>Dataset description</em></p>\n\n<p>- Total size: 26 GB<br>\n- System: B144<br>\n- Temperature(s): 298K<br>\n- Mass density(ies): 2.483 gcc<br>\n- Crystal Structure: amorphous (material mp-160 in the materials project)<br>\n- Number of atomic snapshots: 15<br>\n- Contents:<br>\n - ideal crystal structure: no<br>\n - MD trajectory: no<br>\n - Atomic positions: no<br>\n - DFT inputs: yes<br>\n - DFT outputs (energies): yes<br>\n - SNAP vectors: yes<br>\n - dimensions: 108x108x35x94 (last dimension: first three entries are x,y,z coordinates, data size is 91)<br>\n - units: a.u.<br>\n - LDOS vectors: yes<br>\n - dimensions: 108x108x35x241<br>\n - units: 1/(eV*Angstrom^3)<br>\n - trained networks: yes</p>\n\n<p><br>\n<em>Dataset structure</em></p>\n\n<p>A .zip file is included for each for each of its five type of data:</p>\n\n<p>- ldos.zip: holds the LDOS vectors (one HDF5 file per snapshot)<br>\n- bispectrum.zip: holds the bispectrum fingerprint vectors (one HDF5 file per snapshot)<br>\n- dft_outputs: holds the outputs from the DFT calculations, i.e. energies and simulation parameters in a .json format (one per snapshot)<br>\n- dft_inputs: holds the inputs for the DFT calculations, in the form of a QE input file (one per snapshot)<br>\n- models: holds five trained NN models for the data set</p>",
"datePublished": "2025-05-14",
"@context": "https://schema.org/",
"version": "v1.0.0",
"creator": [
{
"affiliation": "HZDR",
"name": "Fiedler, Lenz",
"@id": "https://orcid.org/0000-0002-8311-0613",
"@type": "Person"
},
{
"affiliation": "HZDR",
"name": "Cangi, Attila",
"@id": "https://orcid.org/0000-0001-9162-262X",
"@type": "Person"
}
],
"url": "https://rodare.hzdr.de/record/3746",
"@type": "Dataset",
"@id": "https://doi.org/10.14278/rodare.3746",
"distribution": [
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"identifier": "https://doi.org/10.14278/rodare.3746",
"sameAs": [
"https://www.hzdr.de/publications/Publ-41336"
],
"keywords": [],
"license": "https://creativecommons.org/licenses/by/4.0/legalcode"
}
| All versions | This version | |
|---|---|---|
| Views | 262 | 262 |
| Downloads | 41 | 41 |
| Data volume | 213.3 GB | 213.3 GB |
| Unique views | 238 | 238 |
| Unique downloads | 25 | 25 |