Predicting novel superconducting hydrides using machine learning approaches
Michael J. Hutcheon, Alice M. Shipley, Richard J. Needs
DOI 10.1103/PhysRevB.101.144505 · Physical Review B
Active bibliographic source — not scientific approval
Bibliographic access preserves source history; it does not approve extracted materials or validate reported claims. Review warnings on each occurrence separately.
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.
Source-reported materials — not catalogue approval
| Formula | Reported Tc (K) | Pressure (GPa) | Type |
|---|---|---|---|
| RbH12 Archive — visibility unverified Source-occurrence policy only; no material identity or catalogue acceptance is inferred from the formula. | 115 | 50 GPa | unknown |
Similar papers
Prediction of high-Tc superconductivity in ternary lanthanum borohydrides
similarity 0.95Xiaowei Liang et al.
Source status unknown — claims are unverified
Computational discovery of a dynamically stable cubic SH3-like high-temperature superconductor at 100 GPa via CH4 intercalation
similarity 0.94Ying Sun et al.
Source status unknown — claims are unverified
Stabilizing a hydrogen-rich superconductor at 1 GPa by charge transfer modulated virtual high-pressure effect
similarity 0.94Miao Gao et al.
Source status unknown — claims are unverified
Machine-learning approach for discovery of conventional superconductors
similarity 0.94Huan Tran & Tuoc N. Vu
Source status unknown — claims are unverified
Predicting novel superconducting hydrides using machine learning approaches
similarity 0.91Michael J. Hutcheon et al. · 2020 · arXiv:2001.09852
Source status unknown — claims are unverified
High-throughput discovery of high-temperature conventional superconductors
similarity 0.91Alice M. Shipley et al.
Source status unknown — claims are unverified