Volume 3 Issue 2
June  2024
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Daniel Wines, Kamal Choudhary. Data-driven design of high pressure hydride superconductors using DFT and deep learning[J]. Materials Futures, 2024, 3(2): 025602. doi: 10.1088/2752-5724/ad4a94
Citation: Daniel Wines, Kamal Choudhary. Data-driven design of high pressure hydride superconductors using DFT and deep learning[J]. Materials Futures, 2024, 3(2): 025602. doi: 10.1088/2752-5724/ad4a94
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Data-driven design of high pressure hydride superconductors using DFT and deep learning

© 2024 The Author(s). Published by IOP Publishing Ltd on behalf of the Songshan Lake Materials Laboratory
Materials Futures, Volume 3, Number 2
  • Received Date: 2024-02-23
  • Accepted Date: 2024-05-12
  • Rev Recd Date: 2024-05-01
  • Publish Date: 2024-05-31
doi: 10.1088/2752-5724/ad4a94

  • Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, United States of America

  • Data availability statement

    Software packages mentioned in the article can be found at https://github.com/usnistgov/jarvis. All code and data specific to this work can be found at https://doi.org/10.6084/m9.figshare.25270012 and model benchmarks can be found at https://pages.nist.gov/jarvis_leaderboard/.

    Notes

    Please note that the use of commercial software (VASP) does not imply recommendation by the National Institute of Standards and Technology.

    Conflicts of interest

    The authors declare no competing interests.

    Author to whom any correspondence should be addressed.

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    Abstract
  • AbstractThe observation of superconductivity in hydride-based materials under ultrahigh pressures (for example, H3S and LaH10) has fueled the interest in a more data-driven approach to discovering new high-pressure hydride superconductors. In this work, we performed density functional theory (DFT) calculations to predict the critical temperature (Tc) of over 900 hydride materials under a pressure range of (0-500) GPa, where we found 122 dynamically stable structures with a Tc above MgB2 (39 K). To accelerate screening, we trained a graph neural network (GNN) model to predict Tc and demonstrated that a universal machine learned force-field can be used to relax hydride structures under arbitrary pressures, with significantly reduced cost. By combining DFT and GNNs, we can establish a more complete map of hydrides under pressure.
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