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Fe-Based Superconducting Transition Temperature Modeling through Gaussian Process Regression

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Abstract

Extensive research has been conducted to find new superconducting materials that exhibit high critical temperature T\(_{c}\), in order to fulfill the needs of practical applications with liquid-helium-free refrigeration or even at room temperature. Iron-based superconductors show high T\(_{c}\) and high upper critical field. The research, however, requires significant manpower for materials synthesis and characterization, and costly equipment and facilities. Computational approaches have contributed greatly to investigate the properties of solid-state matter in many fields, which can be integrated to machine learning and big-data analysis. In this work, the Gaussian process regression model is developed to predict Fe-based superconductor critical temperature based on lattice parameters. This modeling approach demonstrates a high degree of accuracy and stability that lead to the statistical relationship between the lattice parameters and T\(_{c}\). The results disclosed by this work can also lead to a better understanding of the origin of superconductivity in these materials.

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Notes

  1. The introduction of the methodology largely follows, e.g., [29, 31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51].

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    Yun Zhang & Xiaojie Xu

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Zhang, Y., Xu, X. Fe-Based Superconducting Transition Temperature Modeling through Gaussian Process Regression. J Low Temp Phys 202, 205–218 (2021). https://doi.org/10.1007/s10909-020-02545-9

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