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100+ datasets found
  1. m

    Data from: Large-scale machine-learning-assisted exploration of the whole...

    • archive.materialscloud.org
    Updated Oct 4, 2022
  2. m

    The Materials Cloud 2D database (MC2D)

    • archive.materialscloud.org
    Updated Jun 24, 2022
  3. m

    Materials Cloud three-dimensional crystals database (MC3D)

    • archive.materialscloud.org
    Updated Mar 12, 2022
  4. d

    Materials Cloud three-dimensional crystals database (MC3D) - Dataset -...

    • b2find.dkrz.de
    Updated Apr 25, 2020
  5. f

    FAIRsharing record for: Materials Cloud

    • fairsharing.org
    Updated Jun 21, 2018
  6. d

    The Materials Cloud 2D database (MC2D) - Dataset - B2FIND

    • b2find.dkrz.de
    Updated Apr 4, 2023
  7. m

    Data from: Crystal-graph attention networks for the prediction of stable...

    • archive.materialscloud.org
    Updated Dec 16, 2021
  8. Materials Cloud, An Open Science Portal for FAIR Data Sharing

    • figshare.com
    mp4
    Updated Jun 5, 2023
  9. d

    Construction-materials in Cloud County, Kansas (NGMDB)

    • datadiscoverystudio.org
    • catalog.data.gov
    Updated Jan 1, 1951
  10. m

    Medium-range structure of vitreous SiO2

    • archive.materialscloud.org
    Updated Dec 11, 2019
  11. r

    Atom Probe Workbench version 1.0.0: An Australian cloud-based platform for...

    • researchdata.edu.au
    Updated Jan 22, 2014
  12. Supplementary Material for “LiveForen: Ensuring Live Forensic Integrity in...

    • ieee-dataport.org
    Updated Jan 16, 2019
  13. W

    Data from: MATERIALS FOR IN SITU PROCESSING SYSTEMS

    • cloud.csiss.gmu.edu
    • edx.netl.doe.gov
    • +2more
    pdf
    Updated Aug 8, 2019
  14. m

    Point cloud raw data of composite layup with air pockets

    • data.mendeley.com
    Updated Apr 16, 2020
  15. Semiconductor Materials Market - North America, Europe, EMEA, APAC : US,...

    • technavio.com
  16. e

    Cloud Microservices Market Size, Share & Trends Analysis Report By...

    • extrapolate.com
    csv
    Updated Dec 31, 2020
  17. W

    LightMAT

    • cloud.csiss.gmu.edu
    html
    Updated Aug 8, 2019
  18. W

    Downtown Material Loading Zones

    • cloud.csiss.gmu.edu
    • data.raleighnc.gov
    • +1more
    csv, esri rest +4
    Updated Sep 10, 2019
  19. W

    List of Nuclear Materials Licensing Actions Received

    • cloud.csiss.gmu.edu
    • catalog.data.gov
    • +2more
    xls
    Updated Mar 6, 2021
  20. m

    Automated high-throughput Wannierisation

    • archive.materialscloud.org
    Updated Jun 21, 2020
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Cite
Materials Cloud (2022). Large-scale machine-learning-assisted exploration of the whole materials space [Dataset]. http://doi.org/10.24435/materialscloud:m7-50

Data from: Large-scale machine-learning-assisted exploration of the whole materials space

Related Article
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5 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Oct 4, 2022
Dataset provided by
Materials Cloud
Description

Crystal-graph attention networks have emerged recently as remarkable tools for the prediction of thermodynamic stability and materials properties from unrelaxed crystal structures. Previous networks trained on two million materials exhibited, however, strong biases originating from underrepresented chemical elements and structural prototypes in the available data. We tackled this issue computing additional data to provide better balance across both chemical and crystal-symmetry space. Crystal-graph networks trained with this new data show unprecedented generalization accuracy, and allow for reliable, accelerated exploration of the whole space of inorganic compounds. We applied this universal network to performed machine-learning assisted high-throughput materials searches including 2500 binary and ternary prototypes and spanning about 1 billion compounds. After validation using density-functional theory, we uncover in total 19512 additional materials on the convex hull of thermodynamic stability and around 150000 compounds with a distance of less than 50 meV/atom from the hull. Here we include the DCGAT-1, DCGAT-2, and DCGAT-3 datasets used in this work.

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