Extreme Learning Machine Approach to Modeling the Superconducting Critical Temperature of Doped MgB2 Superconductor

Abstract

Magnesium diboride (MgB2) superconductor combines many unique features such as transparency of its grain boundaries to super‐current flow, large coherence length, absence of weak links and small anisotropy. Doping is one of the mechanisms for enhancing these features, as well as the superconducting critical temperature, of the compound. During the process of doping, the MgB2 superconductor structural lattice is often distorted while the room temperature resistivity, as well as residual resistivity ratio, contributes to the impurity scattering in the lattice of doped sam-ples. This work develops three extreme learning machine (ELM)‐based empirical models for deter-mining MgB2 superconducting critical temperature (TC) using structural distortion as contained in lattice parameters (LP) of doped superconductor, room temperature resistivity (RTR) and residual resistivity ratio (RRR) as descriptors. The developed models are compared with nine different existing models in the literature using different performance metrics and show superior performance over the existing models. The developed SINE‐ELM‐RTR model performs better than Intikhab et.al (2021)_linear model, Intikhab et.al (2021)_Exponential model, Intikhab et.al (2021)_Quadratic model, HGA‐SVR‐RRR(2021) model and HGA‐SVR‐CLD(2021) model with a performance improvement of 32.67%, 29.56%, 20.04%, 8.82% and 13.51%, respectively, on the basis of the coefficient of correlation. The established empirical relationships in this contribution will be of immense sig-nificance for quick estimation of the influence of dopants on superconducting transition temperature of MgB2 superconductor without the need for sophisticated equipment while preserving the experimental precision.