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Fe-based superconducting transition temperature modeling by machine learning: A computer science method

Searching for new high temperature superconductors has long been a key research issue. Fe-based superconductors attract researchers’ attention due to their high transition temperature, strong irreversibility field, and excellent crystallographic symmetry. By using doping methods and dopant levels, d...

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Detalles Bibliográficos
Autor principal: Hu, Zhiyuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8346257/
https://www.ncbi.nlm.nih.gov/pubmed/34358265
http://dx.doi.org/10.1371/journal.pone.0255823
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author Hu, Zhiyuan
author_facet Hu, Zhiyuan
author_sort Hu, Zhiyuan
collection PubMed
description Searching for new high temperature superconductors has long been a key research issue. Fe-based superconductors attract researchers’ attention due to their high transition temperature, strong irreversibility field, and excellent crystallographic symmetry. By using doping methods and dopant levels, different types of new Fe-based superconductors are synthesized. The transition temperature is a key indicator to measure whether new superconductors are high temperature superconductors. However, the condition for measuring transition temperature are strict, and the measurement process is dangerous. There is a strong relationship between the lattice parameters and the transition temperature of Fe-based superconductors. To avoid the difficulties in measuring transition temperature, in this paper, we adopt a machine learning method to build a model based on the lattice parameters to predict the transition temperature of Fe-based superconductors. The model results are in accordance with available transition temperatures, showing 91.181% accuracy. Therefore, we can use the proposed model to predict unknown transition temperatures of Fe-based superconductors.
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spelling pubmed-83462572021-08-07 Fe-based superconducting transition temperature modeling by machine learning: A computer science method Hu, Zhiyuan PLoS One Research Article Searching for new high temperature superconductors has long been a key research issue. Fe-based superconductors attract researchers’ attention due to their high transition temperature, strong irreversibility field, and excellent crystallographic symmetry. By using doping methods and dopant levels, different types of new Fe-based superconductors are synthesized. The transition temperature is a key indicator to measure whether new superconductors are high temperature superconductors. However, the condition for measuring transition temperature are strict, and the measurement process is dangerous. There is a strong relationship between the lattice parameters and the transition temperature of Fe-based superconductors. To avoid the difficulties in measuring transition temperature, in this paper, we adopt a machine learning method to build a model based on the lattice parameters to predict the transition temperature of Fe-based superconductors. The model results are in accordance with available transition temperatures, showing 91.181% accuracy. Therefore, we can use the proposed model to predict unknown transition temperatures of Fe-based superconductors. Public Library of Science 2021-08-06 /pmc/articles/PMC8346257/ /pubmed/34358265 http://dx.doi.org/10.1371/journal.pone.0255823 Text en © 2021 Zhiyuan Hu https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Hu, Zhiyuan
Fe-based superconducting transition temperature modeling by machine learning: A computer science method
title Fe-based superconducting transition temperature modeling by machine learning: A computer science method
title_full Fe-based superconducting transition temperature modeling by machine learning: A computer science method
title_fullStr Fe-based superconducting transition temperature modeling by machine learning: A computer science method
title_full_unstemmed Fe-based superconducting transition temperature modeling by machine learning: A computer science method
title_short Fe-based superconducting transition temperature modeling by machine learning: A computer science method
title_sort fe-based superconducting transition temperature modeling by machine learning: a computer science method
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8346257/
https://www.ncbi.nlm.nih.gov/pubmed/34358265
http://dx.doi.org/10.1371/journal.pone.0255823
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