Computational Intelligence Approach for Estimating Superconducting Transition Temperature of Disordered MgB2Superconductors Using Room Temperature Resistivity

Abstract

Doping and fabrication conditions bring about disorder in MgB2superconductor and further influence its room temperature resistivity as well as its superconducting transition temperature (TC). Existence of a model that directly estimatesTCof any doped MgB2superconductor from the room temperature resistivity would have immense significance since room temperature resistivity is easily measured using conventional resistivity measuring instrument and the experimental measurement ofTCwastes valuable resources and is confined to low temperature regime. This work develops a model, superconducting transition temperature estimator (STTE), that directly estimatesTCof disordered MgB2superconductors using room temperature resistivity as input to the model. STTE was developed through training and testing support vector regression (SVR) with ten experimental values of room temperature resistivity and their correspondingTCusing the best performance parameters obtained through test-set cross validation optimization technique. The developed STTE was used to estimateTCof different disordered MgB2superconductors and the obtained results show excellent agreement with the reported experimental data. STTE can therefore be incorporated into resistivity measuring instruments for quick and direct estimation ofTCof disordered MgB2superconductors with high degree of accuracy.