Analysis of process parameters in the laser deposition of YBa2Cu3O7 superconducting films by using SVR

•Proposed new ideas & strategies to improve energy storage density for SMES system.•One is to increase the effective current density in the superconducting coils.•Another is to optimize the configuration of the SMES coil.•A new conceive of energy compression is also proposed.

There are several process parameters in the growth of YBa2Cu3O7 superconducting films by using pulsed laser deposition (PLD). The relationship between the response and process parameters is highly nonlinear and quite complicated. It is very valuable to quantitatively estimate the response under different deposition parameters. In this study, according to an experimental data set on the superconducting transition temperature (Tc) and relative resistance ratio (rR) of 17 samples of YBa2Cu3O7 films deposited under various parameters, the support vector regression (SVR) combined with particle swarm optimization (PSO), was proposed to predict the Tc and rR for YBa2Cu3O7 films. The prediction performance of SVR was compared with that of multiple regression analysis (MRA) models. The results strongly support that the generalization ability of SVR model consistently surpasses that of MRA via leave-one-out cross validation (LOOCV). The mean absolute percentage errors for Tc and rR are 0.37% and 1.51% respectively via LOOCV test of SVR. Sensitivity analysis discovered the most sensitive parameters affecting the Tc and rR. This study suggests that the established SVR model can be used to accurately foresee the Tc and rR. And it can be used to optimizing the deposition parameters in the development of YBa2Cu3O7 films via PLD.