activity
20192021
most citedMachine learning of superconducting critical temperature from Eliashberg theory

5 citations · 5 across the 1 of their papers we have counts for

collaborators

5 papers

cond-mat.supr-con20215 cited

Machine learning of superconducting critical temperature from Eliashberg theory

S. R. Xie, Y. Quan, A. C. Hire +11

The Eliashberg theory of superconductivity accounts for the fundamental physics of conventional electron-phonon superconductors, including the retardation of the interaction and th…

cond-mat.supr-con2021

Towards high-throughput superconductor discovery via machine learning

Stephen R. Xie, Y. Quan, Ajinkya Hire +5

Even though superconductivity has been studied intensively for more than a century, the vast majority of superconductivity research today is carried out in nearly the same manner a…

cond-mat.supr-con2021

High pressure study of low-Z superconductor BeRe

J. Lim, A. C. Hire, Y. Quan +11

With , BeRe exhibits one of the highest critical temperatures among Be-rich compounds. We have carried out a series of high-pressure electrical resi…

cond-mat.mtrl-sci2020

Remarkable low-energy properties of the pseudogapped semimetal BePt

L. Fanfarillo, J. J. Hamlin, R. G. Hennig +7

We report measurements and calculations on the properties of the intermetallic compound BePt. High-quality polycrystalline samples show a nearly constant temperature dependence…

cond-mat.supr-con2019

Functional Form of the Superconducting Critical Temperature from Machine Learning

S. R. Xie, G. R. Stewart, J. J. Hamlin +2

Predicting the critical temperature of new superconductors is a notoriously difficult task, even for electron-phonon paired superconductors for which the theory is relatively…