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© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

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.

Details

Title
Modeling Superconducting Critical Temperature of 122-Iron-Based Pnictide Intermetallic Superconductor Using a Hybrid Intelligent Computational Method
Author
Akomolafe, Oluwatobi 1   VIAFID ORCID Logo  ; Owolabi, Taoreed O 2   VIAFID ORCID Logo  ; Abd Rahman, Mohd Amiruddin 3   VIAFID ORCID Logo  ; Mohd Mustafa Awang Kechik 3   VIAFID ORCID Logo  ; Mohd Najib Mohd Yasin 4   VIAFID ORCID Logo  ; Souiyah, Miloud 5 

 Physics and Electronics Department, Adekunle Ajasin University, Akungba Akoko 342111, Nigeria; [email protected] (O.A.); [email protected] (T.O.O.) 
 Physics and Electronics Department, Adekunle Ajasin University, Akungba Akoko 342111, Nigeria; [email protected] (O.A.); [email protected] (T.O.O.); Department of Physics, Faculty of Science, Universiti Putra Malaysia, Serdang 43400, Malaysia; [email protected] 
 Department of Physics, Faculty of Science, Universiti Putra Malaysia, Serdang 43400, Malaysia; [email protected] 
 Advanced Communication Engineering (ACE), Centre of Excellence, Universiti Malaysia Perlis, Kangar 01000, Malaysia 
 Department of Mechanical Engineering, College of Engineering, University of Hafr Al Batin, P.O. Box 1803, Hafr Al Batin 31991, Saudi Arabia; [email protected] 
First page
4604
Publication year
2021
Publication date
2021
Publisher
MDPI AG
e-ISSN
19961944
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2565380383
Copyright
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.