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

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 of 𝑇𝐶 waste s 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 tothe model. STTE was developed through training and testing support vector regression (SVR) with ten experimental values ofroom 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 MgB2superconductorsand the obtained results show excellent agreement with the reported experimental data. STTE can therefore be incorporated intoresistivity measuring instruments for quick and direct estimation of𝑇𝐶of disordered MgB2superconductors with high degree of accuracy.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 anydoped 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 of 𝑇𝐶 waste s 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 tothe 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 crossvalidation optimization technique. The developed STTE was used to estimate𝑇𝐶of different disordered MgB2superconductorsand the obtained results show excellent agreement with the reported experimental data. STTE can therefore be incorporated in to resistivity measuring instruments for quick and direct estimation of𝑇𝐶of disordered MgB2superconductors with high degree ofaccuracy.