Modeling superconducting transition temperature of doped MgB2 superconductor from structural distortion and ambient temperature resistivity measurement using hybrid intelligent approach

Abstract

Improvement of superconducting properties of MgB2 superconductor becomes necessary in several medium and large scale commercial practical applications where generation of high magnetic field is of significant interest. High power cables, energy storage devices, wind turbines and magnetic resonance imaging are the potential areas of application where MgB2 superconductor with improved property is indispensable. In order to facilitate quick determination of transition temperature of MgB2 superconductor and to determine the conditions at which this superconducting property is optimal for the desired application, this present contribution hybridizes genetic algorithm (HGA) with support vector regression (SVR) machine learning to model the transition temperature of doped MgB2 superconductor using ambient room temperature resistivity (RTR), residual resistivity ratio (RRR) and structural lattice distortion (SLD) due to the incorporated dopants as descriptors. The developed HGA-SVR-RTR model that utilizes RTR as descriptor shows better performance than the proposed HGA-SVR-RRR model (that employs RRR as descriptor) and HGA-SVR-SLD model (that implements SLD as descriptors) with improvement in performance by 88.45% and 71.41%, respectively using mean absolute error (MAE) as a parameter that evaluates the model performance. The developed HGA-SVR-RTR model also outperforms the existing models such as GPR-prediction (2020), STTE model (2016) and STTE model (2014) with performance improvement of 74.85%, 74.76% and 92.96%, respectively using MAE as a yardstick for performance comparison. The developed HGA-SVR-RRR model performs better than HGA-SVR-SLD model with percentage improvement of 59.6% on the basis of MAE. The performance of the developed models would definitely facilitate cost effective search for doped MgB2 superconductor for desired application without experimental stress.