Machine learning of superconducting critical temperature from Eliashberg theory
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{
"updated": "2021-12-06T14:16:33.033797+00:00",
"id": "1030",
"metadata": {
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"status": "published",
"_files": [
{
"description": "Data tables including model inputs, prefactors, and critical temperature predictions. Data is partitioned by data source: Allen-Dynes 1975, Gaussian-based artificial, EPW-calculated, literature, and hydrides.",
"size": 429059,
"key": "data_tables.zip",
"checksum": "md5:86c68504d1780b6fc7eaccbd4116706c"
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{
"description": "Inputs and outputs of the SISSO framework for both rounds of symbolic regression. Please see https://github.com/henniggroup/symbolic-regression-utilities for tools to create and analyze SISSO inputs and outputs.",
"size": 7810599,
"key": "sisso_runs.zip",
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"contributors": [
{
"givennames": "Stephen",
"familyname": "Xie",
"affiliations": [
"Department of Materials Science and Engineering, University of Florida, USA",
"Quantum Theory Project, University of Florida, USA"
],
"email": "sxiexie@gmail.com"
},
{
"givennames": "Yundi",
"familyname": "Quan",
"affiliations": [
"Department of Materials Science and Engineering, University of Florida, USA",
"Quantum Theory Project, University of Florida, USA",
"Department of Physics, University of Florida, USA"
]
},
{
"givennames": "Ajinkya",
"familyname": "Hire",
"affiliations": [
"Department of Materials Science and Engineering, University of Florida, USA",
"Quantum Theory Project, University of Florida, USA"
]
},
{
"givennames": "Boning",
"familyname": "Deng",
"affiliations": [
"Department of Physics, University of Florida, USA"
]
},
{
"givennames": "Jonathan",
"familyname": "DeStefano",
"affiliations": [
"Department of Physics, University of Florida, USA"
]
},
{
"givennames": "Ian",
"familyname": "Salinas",
"affiliations": [
"Department of Physics, University of Florida, USA"
]
},
{
"givennames": "Urja",
"familyname": "Shah",
"affiliations": [
"Department of Physics, University of Florida, USA"
]
},
{
"givennames": "Laura",
"familyname": "Fanfarillo",
"affiliations": [
"Department of Physics, University of Florida, USA"
]
},
{
"givennames": "Jinhyuk",
"familyname": "Lim",
"affiliations": [
"Department of Physics, University of Florida, USA"
]
},
{
"givennames": "Jungsoo",
"familyname": "Kim",
"affiliations": [
"Department of Physics, University of Florida, USA"
]
},
{
"givennames": "Gregory",
"familyname": "Stewart",
"affiliations": [
"Department of Physics, University of Florida, USA"
]
},
{
"givennames": "James",
"familyname": "Hamlin",
"affiliations": [
"Department of Physics, University of Florida, USA"
]
},
{
"givennames": "Peter",
"familyname": "Hirschfeld",
"affiliations": [
"Department of Physics, University of Florida, USA"
]
},
{
"givennames": "Richard",
"familyname": "Hennig",
"affiliations": [
"Department of Materials Science and Engineering, University of Florida, USA",
"Quantum Theory Project, University of Florida, USA"
]
}
],
"conceptrecid": "1029",
"doi": "10.24435/materialscloud:68-6p",
"references": [
{
"url": "https://arxiv.org/abs/2106.05235",
"citation": "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, arXiv:2106.05235 (2021).",
"comment": "Preprint where the data is discussed.",
"type": "Preprint"
}
],
"title": "Machine learning of superconducting critical temperature from Eliashberg theory",
"publication_date": "Sep 18, 2021, 00:25:40",
"description": "The Eliashberg theory of superconductivity accounts for the fundamental physics of conventional electron-phonon superconductors, including the retardation of the interaction and the effect of the Coulomb pseudopotential, to predict the critical temperature Tc and other properties. McMillan, Allen, and Dynes derived approximate closed-form expressions for the critical temperature predicted by this theory, which depends essentially on the electron-phonon spectral function \u03b1\u00b2F(\u03c9), using \u03b1\u00b2F for low-Tc superconductors. Here we show that modern machine learning techniques can substantially improve these formulae, accounting for more general shapes of the \u03b1\u00b2F function. Using symbolic regression and the sure independence screening and sparsifying operator (SISSO) framework, together with a database of artificially generated \u03b1\u00b2F functions, ranging from multimodal Einstein-like models to calculated spectra of polyhydrides, as well as numerical solutions of the Eliashberg equations, we derive a formula for Tc that performs as well as Allen-Dynes for low-Tc superconductors and substantially better for higher-Tc ones. The expression identified through our data-driven approach corrects the systematic underestimation of Tc while reproducing the physical constraints originally outlined by Allen and Dynes. This equation should replace the Allen-Dynes formula for the prediction of higher-temperature superconductors and the estimation of \u03bb from experimental data.\n\nThis repository contains CSV tables of model inputs, prefactors, and critical temperature predictions used for machine learning and validation in this work. Additionally, the inputs and outputs of the SISSO framework for both rounds of symbolic regression are included. Please see https://github.com/henniggroup/symbolic-regression-utilities for tools to create and analyze SISSO inputs and outputs.",
"mcid": "2021.150",
"edited_by": 100,
"version": 1,
"is_last": true,
"owner": 531,
"license_addendum": null,
"keywords": [
"supercondcutivity",
"symbolic regression",
"machine learning"
],
"_oai": {
"id": "oai:materialscloud.org:1030"
},
"license": "Apache License 2.0"
},
"revision": 6,
"created": "2021-09-17T18:39:17.610006+00:00"
}