Modeling Superconducting Critical Temperature of 122-Iron-Based Pnictide Intermetallic Superconductor Using a Hybrid Intelligent Computational Method

Abstract

Structural transformation and magnetic ordering interplays for emergence as well as suppression of superconductivity in 122-iron-based superconducting materials. Electron and hole doping play a vital role in structural transition and magnetism suppression and ultimately enhance the room pressure superconducting critical temperature of the compound. This work models the superconducting critical temperature of 122-iron-based superconductor using tetragonal to orthorhombic lattice (LAT) structural transformation during low-temperature cooling and ionic radii of the dopants as descriptors through hybridization of support vector regression (SVR) intelligent algorithm with particle swarm (PS) parameter optimization method. The developed PS-SVR-RAD model, which utilizes ionic radii (RAD) and the concentrations of dopants as descriptors, shows better performance over the developed PS-SVR-LAT model that employs lattice parameters emanated from structural transformation as descriptors. Using the root mean square error (RMSE), coefficient of correlation (CC) and mean absolute error as performance measuring criteria, the developed PS-SVR-RAD model performs better than the PS-SVR-LAT model with performance improvement of 15.28, 7.62 and 72.12%, on the basis of RMSE, CC and Mean Absolute Error (MAE), respectively. Among the merits of the developed PS-SVR-RAD model over the PS-SVR-LAT model is the possibility of electrons and holes doping from four different dopants, better performance and ease of model development at relatively low cost since the descriptors are easily fetched ionic radii. The developed intelligent models in this work would definitely facilitate quick and precise determination of critical transition temperature of 122-iron-based superconductor for desired applications at low cost with experimental stress circumvention.

Artificial intelligence methods for applied superconductivity: material, design, manufacturing, testing, operation, and condition monitoring

“…To do this, tetragonal to orthorhombic lattice structural transformation or RAD as descriptors were used. Consequently, the obtained prediction accuracy was between 86.37% and 98.97% [313]. These results show that the algorithm achieves a better performance when RAD is designated as a descriptor.…”

Modeling Temperature-Dependent Thermoelectric Performance of Magnesium-Based Compounds for Energy Conversion Efficiency Enhancement Using Intelligent Computational Methods

“…Support vector regression (SVR) effectively addresses and characterizes non-linear systems as a result of the robust underlying structural risk minimization principle inherent to the algorithm [34][35][36]. Given thermoelectric magnesium-based materials β k , τ * k n k , β k represents the input descriptive vectors, which include the operating temperature, ionic radii of the elemental compositions as well as the elemental concentrations, while τ denotes the corresponding measured thermoelectric figure of merit.…”

Tailoring the Energy Harvesting Capacity of Zinc Selenide Semiconductor Nanomaterial through Optical Band Gap Modeling Using Genetically Optimized Intelligent Method

“…The utilization of the kernel trick, convex optimization, and Langrage multipliers enhances the global convergence of the algorithm, and subsequently averts local solutions. These unique features have significantly contributed to the wide application of this algorithm in different areas of study [11][12][13][14]. In an attempt to further enhance the robustness and reliability of SVR-based models, the user-defined parameters of the algorithm are optimized using a genetic algorithm (GA) in this work, with an evolutionary operational principle [15,16].…”