Yttrium barium copper oxide superconducting transition temperature modeling through gaussian process regression

The high-temperature superconductor, YBa2Cu3O7-x (YBCO), is a promising candidate for high field magnet fabrication as it has critical temperature, T-c, of over 80 K and an upper critical field over 100 T. In practical applications, the quality and stability of a superconducting magnet depends heavily on T-c. Extensive research has been conducted to modify crystal structures of YBCO materials by chemical substitution and doping in order to enhance the superconducting transition temperature. The increase in T-c fulfills the needs of practical applications with liquid-helium-free refrigeration and a delay in magnet failure. But the research requires significant manpower for materials synthesis, characterization, and quench detection, as well as costly equipment and facilities. In this work, the Gaussian process regression model is developed to predict YBCO superconducting transition temperature based on lattice parameters. Results here show a high correlation coefficient (99.78%) between the predicted and experimental superconducting transition temperature, a low prediction root mean square error (1.04% of the sample mean) and mean absolute error (0.27% of the sample mean), and stable model performance. This modeling approach contributes to efficient and low-cost estimations of superconducting transition temperature and understandings of the temperature based on lattice parameters.