Modeling superconducting critical temperature of 122-iron-based pnictide intermetallic superconductor using a hybrid intelligent computational method
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 sup...
| Main Authors: | , , , , , |
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| Format: | Article |
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MDPI
2021
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| Online Access: | http://psasir.upm.edu.my/id/eprint/94219/ |
| _version_ | 1848861939108675584 |
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| author | Akomolafe, Oluwatobi Owolabi, Taoreed O. Abd Rahman, Mohd Amiruddin Awang Kechik, Mohd Mustafa Mohd Yasin, Mohd Najib Souiyah, Miloud |
| author_facet | Akomolafe, Oluwatobi Owolabi, Taoreed O. Abd Rahman, Mohd Amiruddin Awang Kechik, Mohd Mustafa Mohd Yasin, Mohd Najib Souiyah, Miloud |
| author_sort | Akomolafe, Oluwatobi |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T13:09:05Z |
| format | Article |
| id | upm-94219 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:09:05Z |
| publishDate | 2021 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-942192023-05-08T04:57:14Z http://psasir.upm.edu.my/id/eprint/94219/ Modeling superconducting critical temperature of 122-iron-based pnictide intermetallic superconductor using a hybrid intelligent computational method Akomolafe, Oluwatobi Owolabi, Taoreed O. Abd Rahman, Mohd Amiruddin Awang Kechik, Mohd Mustafa Mohd Yasin, Mohd Najib Souiyah, Miloud 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. MDPI 2021-08-16 Article PeerReviewed Akomolafe, Oluwatobi and Owolabi, Taoreed O. and Abd Rahman, Mohd Amiruddin and Awang Kechik, Mohd Mustafa and Mohd Yasin, Mohd Najib and Souiyah, Miloud (2021) Modeling superconducting critical temperature of 122-iron-based pnictide intermetallic superconductor using a hybrid intelligent computational method. Materials, 14 (16). art. no. 4604. pp. 1-14. ISSN 1996-1944 https://www.mdpi.com/1996-1944/14/16/4604 10.3390/ma14164604 |
| spellingShingle | Akomolafe, Oluwatobi Owolabi, Taoreed O. Abd Rahman, Mohd Amiruddin Awang Kechik, Mohd Mustafa Mohd Yasin, Mohd Najib Souiyah, Miloud Modeling superconducting critical temperature of 122-iron-based pnictide intermetallic superconductor using a hybrid intelligent computational method |
| title | Modeling superconducting critical temperature of 122-iron-based pnictide intermetallic superconductor using a hybrid intelligent computational method |
| title_full | Modeling superconducting critical temperature of 122-iron-based pnictide intermetallic superconductor using a hybrid intelligent computational method |
| title_fullStr | Modeling superconducting critical temperature of 122-iron-based pnictide intermetallic superconductor using a hybrid intelligent computational method |
| title_full_unstemmed | Modeling superconducting critical temperature of 122-iron-based pnictide intermetallic superconductor using a hybrid intelligent computational method |
| title_short | Modeling superconducting critical temperature of 122-iron-based pnictide intermetallic superconductor using a hybrid intelligent computational method |
| title_sort | modeling superconducting critical temperature of 122-iron-based pnictide intermetallic superconductor using a hybrid intelligent computational method |
| url | http://psasir.upm.edu.my/id/eprint/94219/ http://psasir.upm.edu.my/id/eprint/94219/ http://psasir.upm.edu.my/id/eprint/94219/ |