Predicting doped MgB2 superconductor critical temperature from lattice parameters using Gaussian process regression

• Published in: Physica. C, Superconductivity Vol. 573; p. 1353633
• Main Authors: Zhang, Yun, Xu, Xiaojie
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 15.06.2020; Elsevier BV
• Subjects: Anisotropy; Critical temperature; Disorders; Gaussian process; Gaussian process regression; Grain boundaries; Lattice parameters; Magnesium compounds; MgB2; Normal distribution; Physical properties; Process parameters; Raw materials; Regression models; Semiconductor doping; Superconductivity; Superconductors; Transition temperature; Gaussian process regression; Superconductors; Critical temperature; Lattice parameters; MgB2
• ISSN: 0921-4534; 1873-2143
• DOI: 10.1016/j.physc.2020.1353633

Magnesium boride, MgB2, has attracted much attention since the discovery of its superconductivity in 2001. The absence of weak-links in grain boundaries, less prominent anisotropy, rather simple powder-in-tube wire fabrication techniques, and much lower prices of raw materials, have made this new superconducting material a promising candidate for high-field magnet applications. Furthermore, it has been demonstrated that various methods, such as chemical doping, irradiations, and different processing parameters, can lead to lattice disorders in the materials and thus alter physical properties. Empirical results have shown that changes in lattice parameters through various methods correlate with changes in Tc but correlations are merely general tendencies and obviously not universal. In this work, the Gaussian process regression model is developed to predict critical temperature based on lattice parameters among disordered MgB2 in various materials systems. This modeling approach demonstrates a high degree of accuracy and stability, contributing to efficient and low-cost predictions of Tc and understandings of disorders and superconductivity in MgB2 superconductors.