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

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  • Zhiyuan Hu

Abstract

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.

Suggested Citation

  • Zhiyuan Hu, 2021. "Fe-based superconducting transition temperature modeling by machine learning: A computer science method," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-12, August.
    • Handle: RePEc:plo:pone00:0255823
    DOI: 10.1371/journal.pone.0255823
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