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 samples. This work develops three extreme learning machine (ELM)-based empirical models for determining 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 significance 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.
Highlights
Discovery of superconductivity behavior in magnesium diboride (MgB2) widened the potentials of the material for superconductor applications, such as liquid-helium-free magnetic resonance imaging systems, generators, transformers and fault current limiters, among others [1,2,3,4]
It should be noted that similar computational strategies were adopted for each of the three developed models except that residual resistivity ratio (RRR) was the descriptor for the SIG-extreme learning machine (ELM)-RRR model with sigmoid activation function, and room temperature resistivity (RTR) served as the descriptor for the SINE-ELM-RTR model with sine activation function, while the SINE-ELM-LP employed lattice parameters as descriptors with sine activation function
The superconducting critical temperature of MgB2 superconductor doped with foreign materials was modeled in this work using an extreme learning machine (ELM) intelligence method
Summary
Discovery of superconductivity behavior in magnesium diboride (MgB2) widened the potentials of the material for superconductor applications, such as liquid-helium-free magnetic resonance imaging systems, generators, transformers and fault current limiters, among others [1,2,3,4]. Other unique features of this compound include transparency of its grain boundaries to super-current flow, large coherence length, absence of weak links and small anisotropy [6,7]. These unique features make MgB2 superconductor admirable, despite its lower superconducting transition temperature (TC), compared to many conventional high-temperature superconductors. Among the aims of this research is the establishment of a relationship between lattice distortion and MgB2 superconducting critical temperature using extreme learning machine computational methods so as to enhance quick characterization of MgB2 superconductor without the need for costly cryogenic devices
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