May 14, 2025 Dataset Open Access

Fiedler, Lenz; Cangi, Attila

Dublin Core Export

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Fiedler, Lenz</dc:creator>
  <dc:creator>Cangi, Attila</dc:creator>
  <dc:date>2025-05-14</dc:date>
  <dc:description>Boron data set for machine learning applications

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.

This dataset contains one .zip file for each of its five type of data (bispectrum descriptors, LDOS, DFT inputs, DFT outputs and trained models).

Authors:

- Fiedler, Lenz (HZDR / CASUS)
- Cangi, Attila (HZDR / CASUS)

Affiliations:

HZDR - Helmholtz-Zentrum Dresden-Rossendorf
CASUS - Center for Advanced Systems Understanding

Dataset description

- Total size: 26 GB
- System: B144
- Temperature(s): 298K
- Mass density(ies): 2.483 gcc
- Crystal Structure: amorphous (material mp-160 in the materials project)
- Number of atomic snapshots: 15
- Contents:
    - ideal crystal structure: no
    - MD trajectory: no
    - Atomic positions: no
    - DFT inputs: yes
    - DFT outputs (energies): yes
    - SNAP vectors: yes
        - dimensions: 108x108x35x94 (last dimension: first three entries are x,y,z coordinates, data size is 91)
        - units: a.u.
    - LDOS vectors: yes
        - dimensions: 108x108x35x241
        - units: 1/(eV*Angstrom^3)
    - trained networks: yes


Dataset structure

A .zip file is included for each for each of its five type of data:

- ldos.zip: holds the LDOS vectors (one HDF5 file per snapshot)
- bispectrum.zip: holds the bispectrum fingerprint vectors  (one HDF5 file per snapshot)
- dft_outputs: holds the outputs from the DFT calculations, i.e. energies and simulation parameters in a .json format (one per snapshot)
- dft_inputs: holds the inputs for the DFT calculations, in the form of a QE input file (one per snapshot)
- models: holds five trained NN models for the data set</dc:description>
  <dc:identifier>https://rodare.hzdr.de/record/3746</dc:identifier>
  <dc:identifier>10.14278/rodare.3746</dc:identifier>
  <dc:identifier>oai:rodare.hzdr.de:3746</dc:identifier>
  <dc:relation>url:https://www.hzdr.de/publications/Publ-41336</dc:relation>
  <dc:relation>url:https://www.hzdr.de/publications/Publ-40059</dc:relation>
  <dc:relation>doi:10.14278/rodare.3745</dc:relation>
  <dc:relation>url:https://rodare.hzdr.de/communities/rodare</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:title>Boron data set for machine learning applications</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>dataset</dc:type>
</oai_dc:dc>