Predicting novel superconducting hydrides using machine learning approaches

Michael J. Hutcheon, Alice M. Shipley, and Richard J. Needs
Phys. Rev. B 101, 144505 – Published 22 April 2020
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Abstract

The search for superconducting hydrides has, so far, largely focused on finding materials exhibiting the highest possible critical temperatures (Tc). This has led to a bias toward materials stabilized at very high pressures, which introduces a number of technical difficulties in experiment. Here we apply machine learning methods in an effort to identify superconducting hydrides that can operate closer to ambient conditions. The output of these models informs subsequent crystal structure searches, from which we identify stable metallic candidates prior to performing electron-phonon calculations to obtain Tc. Hydrides of alkali and alkaline earth metals are identified as especially promising; of particular note, a Tc of up to 115 K is calculated for RbH12 at 50 GPa, which extends the operational pressure-temperature range occupied by hydride superconductors toward ambient conditions.

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  • Received 27 January 2020
  • Revised 27 March 2020
  • Accepted 27 March 2020

DOI:https://doi.org/10.1103/PhysRevB.101.144505

©2020 American Physical Society

Physics Subject Headings (PhySH)

Condensed Matter, Materials & Applied Physics

Authors & Affiliations

Michael J. Hutcheon*, Alice M. Shipley, and Richard J. Needs

  • Theory of Condensed Matter Group, Cavendish Laboratory, J. J. Thomson Avenue, Cambridge CB3 0HE, United Kingdom

  • *mjh261@cam.ac.uk
  • ams277@cam.ac.uk

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Issue

Vol. 101, Iss. 14 — 1 April 2020

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