Modeling study of the magnetocaloric behavior of La₀.₆₇Ca₀.₃₃Mn₀.₉₈Ni₀.₀₂O₃ alloys using artificial neural networks
The growing interest in environmentally friendly cooling technologies has intensified the search for efficient magnetocaloric materials (MCMs). In this context, accurately predicting the magnetocaloric effect is essential for accelerating materials discovery and reducing experimental efforts. In thi...
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Elsevier
2025-06-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025011338 |
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| author | Kawther Laajimi Emna Ammar Elhadjamor Radhouane Laajimi Mehdi Rahmani Mohamed Hichem Gazzah |
| author_facet | Kawther Laajimi Emna Ammar Elhadjamor Radhouane Laajimi Mehdi Rahmani Mohamed Hichem Gazzah |
| author_sort | Kawther Laajimi |
| collection | DOAJ |
| description | The growing interest in environmentally friendly cooling technologies has intensified the search for efficient magnetocaloric materials (MCMs). In this context, accurately predicting the magnetocaloric effect is essential for accelerating materials discovery and reducing experimental efforts. In this study, we introduce novel approaches for modeling magnetocaloric materials using artificial neural networks (ANNs). Our method aims to establish a standardized framework to evaluate the magnetocaloric effect of polycrystalline La₀.₆₇Ca₀.₃₃Mn₀.₉₈Ni₀.₀₂O₃ alloys obtained by sol-gel method. A multilayer perceptron regression model was used to predict entropy values with temperature and magnetic field variations. The model showed outstanding performance with a coefficient of determination (R²) of 0.999241 and a root mean square error (MSE) of 0.003472. In addition, the variation of heat capacity (ΔCp) under different magnetic fields was successfully predicted using the same approach, yielding a low median absolute error (MedAE) of 0.008. Our results demonstrate the potential of ANNs model to accurately approximate magnetic experimental data and highlight their ability to significantly streamline the design and optimization of newly developed MCMs. |
| format | Article |
| id | doaj-art-4d41aff4f0f946bca2770e893d115474 |
| institution | DOAJ |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-4d41aff4f0f946bca2770e893d1154742025-08-20T02:55:56ZengElsevierResults in Engineering2590-12302025-06-012610505810.1016/j.rineng.2025.105058Modeling study of the magnetocaloric behavior of La₀.₆₇Ca₀.₃₃Mn₀.₉₈Ni₀.₀₂O₃ alloys using artificial neural networksKawther Laajimi0Emna Ammar Elhadjamor1Radhouane Laajimi2Mehdi Rahmani3Mohamed Hichem Gazzah4Laboratory of Quantum and Statistical Physics, Department of Physics, Faculty of Sciences of Monastir, University of Monastir, Environment Street 5019 Monastir, TunisiaRIADI Laboratory-ENSI, Manouba University, Manouba, TunisiaLaboratory of Quantum and Statistical Physics, Department of Physics, Faculty of Sciences of Monastir, University of Monastir, Environment Street 5019 Monastir, Tunisia; Higher Institute of Applied Sciences and Technology of Kairouan, University of Kairouan, Dar El Amen University Campus, 3100 Kairouan, TunisiaHigher Institute of Applied Sciences and Technology of Kairouan, University of Kairouan, Dar El Amen University Campus, 3100 Kairouan, Tunisia; Laboratory of Nanomaterials, Nanotechnology and Energy, Department of Physics, Faculty of Sciences of Tunis, University of Tunis, El Manar, 2092 El Manar, Tunis, Tunisia; Corresponding author.Laboratory of Quantum and Statistical Physics, Department of Physics, Faculty of Sciences of Monastir, University of Monastir, Environment Street 5019 Monastir, TunisiaThe growing interest in environmentally friendly cooling technologies has intensified the search for efficient magnetocaloric materials (MCMs). In this context, accurately predicting the magnetocaloric effect is essential for accelerating materials discovery and reducing experimental efforts. In this study, we introduce novel approaches for modeling magnetocaloric materials using artificial neural networks (ANNs). Our method aims to establish a standardized framework to evaluate the magnetocaloric effect of polycrystalline La₀.₆₇Ca₀.₃₃Mn₀.₉₈Ni₀.₀₂O₃ alloys obtained by sol-gel method. A multilayer perceptron regression model was used to predict entropy values with temperature and magnetic field variations. The model showed outstanding performance with a coefficient of determination (R²) of 0.999241 and a root mean square error (MSE) of 0.003472. In addition, the variation of heat capacity (ΔCp) under different magnetic fields was successfully predicted using the same approach, yielding a low median absolute error (MedAE) of 0.008. Our results demonstrate the potential of ANNs model to accurately approximate magnetic experimental data and highlight their ability to significantly streamline the design and optimization of newly developed MCMs.http://www.sciencedirect.com/science/article/pii/S2590123025011338Artificial neural networksCeramic alloys materialMagnetizationEntropy prediction |
| spellingShingle | Kawther Laajimi Emna Ammar Elhadjamor Radhouane Laajimi Mehdi Rahmani Mohamed Hichem Gazzah Modeling study of the magnetocaloric behavior of La₀.₆₇Ca₀.₃₃Mn₀.₉₈Ni₀.₀₂O₃ alloys using artificial neural networks Results in Engineering Artificial neural networks Ceramic alloys material Magnetization Entropy prediction |
| title | Modeling study of the magnetocaloric behavior of La₀.₆₇Ca₀.₃₃Mn₀.₉₈Ni₀.₀₂O₃ alloys using artificial neural networks |
| title_full | Modeling study of the magnetocaloric behavior of La₀.₆₇Ca₀.₃₃Mn₀.₉₈Ni₀.₀₂O₃ alloys using artificial neural networks |
| title_fullStr | Modeling study of the magnetocaloric behavior of La₀.₆₇Ca₀.₃₃Mn₀.₉₈Ni₀.₀₂O₃ alloys using artificial neural networks |
| title_full_unstemmed | Modeling study of the magnetocaloric behavior of La₀.₆₇Ca₀.₃₃Mn₀.₉₈Ni₀.₀₂O₃ alloys using artificial neural networks |
| title_short | Modeling study of the magnetocaloric behavior of La₀.₆₇Ca₀.₃₃Mn₀.₉₈Ni₀.₀₂O₃ alloys using artificial neural networks |
| title_sort | modeling study of the magnetocaloric behavior of la₀ ₆₇ca₀ ₃₃mn₀ ₉₈ni₀ ₀₂o₃ alloys using artificial neural networks |
| topic | Artificial neural networks Ceramic alloys material Magnetization Entropy prediction |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025011338 |
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