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
  • Publish Date: 2024-05-31
  • The 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 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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