Machine learning of superconducting critical temperature from Eliashberg theory

Machine learning of superconducting critical temperature from Eliashberg theory

Abstract The Eliashberg theory of superconductivity accounts for the fundamental physics of conventional superconductors, including the retardation of the interaction and the Coulomb pseudopotential, to predict the critical temperature T c. McMillan, Allen, and Dynes derived approximate closed-form...

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Bibliographic Details
Main Authors: S. R. Xie, Y. Quan, A. C. Hire, B. Deng, J. M. DeStefano, I. Salinas, U. S. Shah, L. Fanfarillo, J. Lim, J. Kim, G. R. Stewart, J. J. Hamlin, P. J. Hirschfeld, R. G. Hennig
Format: Article
Language:English
Published: Nature Portfolio 2022-01-01
Series:npj Computational Materials
Online Access:https://doi.org/10.1038/s41524-021-00666-7

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https://doi.org/10.1038/s41524-021-00666-7

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