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Data-driven Design of High Pressure Hydride Superconductors using DFT and Deep Learning

Published

Author(s)

Daniel Wines, Kamal Choudhary

Abstract

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 to 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.
Citation
IOP Material Futures

Citation

Wines, D. and Choudhary, K. (2024), Data-driven Design of High Pressure Hydride Superconductors using DFT and Deep Learning, IOP Material Futures, [online], https://doi.org/10.1088/2752-5724/ad4a94, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=957083 (Accessed June 13, 2024)

Issues

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Created May 13, 2024, Updated May 21, 2024