Support Vector Regression Ensemble for Effective Modeling of Magnetic Ordering Temperature of Doped Manganite in Magnetic Refrigeration

Owolabi, Taoreed O.; Akande, Kabiru O.; Olatunji, Sunday O.; Aldhafferi, Nahier; Alqahtani, Abdullah

doi:10.1007/s10909-019-02153-2

Cited by 7 publications

(6 citation statements)

References 61 publications

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“…Step II: Calculation of agents' masses. Using the minimum and maximum values of the fitness of the entire population, the mass of each of the agents is computed at the ith iteration through the implementation of Equations (6) and 7:…”

Section: Physical Principles Of the Gravitational Search Algorithmmentioning

confidence: 99%

“…Step IV: Inertial mass of the agents in the population and gravitational pull computation. The inertial mass is computed using Equations (6) and (7). The Newtonian gravitation pull is computed using Equation (8).…”

Section: Computational Hybridization Of the Gravitational Search And mentioning

confidence: 99%

“…Magnetocaloric compounds are of technological and scientific interest mainly because of their significance in magnetic refrigeration, which has shown high potential in replacing the conventional compression-expansion cycle of a gas cooling system [1][2][3][4]. These compounds are significant in magnetic refrigeration technology since they serve as the refrigerants and the major component of the cooling system characterized with lower noise, a high degree of compactness, high cooling efficiency and environmental friendliness as they do not require ozone-depleting gases [5,6]. Before the discovery of manganite-based compounds as a potential source of a high magnetocaloric effect (which is a physical phenomenon that measures the change in internal temperature of a compound when subjected to adiabatic magnetization), gadolinium was among the well-explored elements with significant magnetocaloric effect near room temperature [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…Some screening approaches to developing and identifying materials with a large magnetocaloric effect have been proposed in the literature [19], [20]. However, machine learning techniques have been identified as effective and efficient tools for determining the influence of dopants on the physical properties (such as magnetic ordering temperature, relative cooling power and magnetocaloric effect) of doped manganite-based compounds [6,[21][22][23][24][25]. The proposed grid search-based extreme learning machine (GS-ELM) and hybrid gravitational search-based support vector regression model the relationship between lattice distortion as well as elemental compositions on the MMEC when dopants are incorporated into the crystal structure of the parent manganite-based compounds.…”

Section: Introductionmentioning

confidence: 99%

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Modeling the Maximum Magnetic Entropy Change of Doped Manganite Using a Grid Search-Based Extreme Learning Machine and Hybrid Gravitational Search-Based Support Vector Regression

Shamsah

Owolabi

2020

Crystals

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The thermal response of a magnetic solid to an applied magnetic field constitutes magnetocaloric effect. The maximum magnetic entropy change (MMEC) is one of the quantitative parameters characterizing this effect, while the magnetic solids exhibiting magnetocaloric effect have great potential in magnetic refrigeration technology as they offer a green solution to the known pollutant-based refrigerants. In order to determine the MMEC of doped manganite and the influence of dopants on the magnetocaloric effect of doped manganite compounds, this work developed a grid search (GS)-based extreme learning machine (ELM) and hybrid gravitational search algorithm (GSA)-based support vector regression (SVR) for estimating the MMEC of doped manganite compounds using ionic radii and crystal lattice parameters as descriptors. Based on the root-mean-square error (RMSE), the developed GSA-SVR-radii model performs better than the existing genetic algorithm (GA)-SVR-ionic model in the literature by 27.09%, while the developed GSA-SVR-crystal model performs better than the existing GA-SVR-lattice model in the literature by 38.34%. Similarly, the developed ELM-GS-crystal model performs better than the existing GA-SVR-ionic model with a performance enhancement of 14.39% and 20.65% using the mean absolute error (MAE) and RMSE, respectively, as performance measuring parameters. The developed models also perform better than the existing models using correlation coefficient as the performance measuring parameter when validated with experimentally measured MMEC. The superior performance of the present models coupled with easy accessibility of the descriptors definitely will facilitate the synthesis of doped manganite compounds with a high magnetocaloric effect without experimental stress.

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Section: Physical Principles Of the Gravitational Search Algorithmmentioning

confidence: 99%

Section: Computational Hybridization Of the Gravitational Search And mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling the Maximum Magnetic Entropy Change of Doped Manganite Using a Grid Search-Based Extreme Learning Machine and Hybrid Gravitational Search-Based Support Vector Regression

Shamsah

Owolabi

2020

Crystals

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“…To develop a predictive model, SVR makes use of a collection of training data that contains predictor variables as well as their corresponding observed responses. By relying solely on the predictor variables, the resulting SVR model can accurately generalize to future unknown data [91]. SVR is based on sound mathematical theory and its optimization problem has an optimal and global solution in the form of linearly constrained quadratic programming [92].…”

Section: Mathematical Formulation and Backgroundmentioning

confidence: 99%

Modeling Energy Gap of Doped Tin (II) Sulfide Metal Semiconductor Nanocatalyst Using Genetic Algorithm‐Based Support Vector Regression

Okoye

Azi

Owolabi

et al. 2022

Journal of Nanomaterials

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Tin (II) sulfide (SnS) is a metal chalcogenide semiconducting material with fascinating and admirable physical features for practical applications in solid-state batteries, photodetectors, gas sensors, optoelectronic devices, emission transistors, and photocatalysis among others. The energy gap of SnS semiconductor nanomaterial that facilitates its usefulness in many applications can be adjusted through dopant incorporation which results in crystal lattice distortion at various crystallite sizes of the semiconductor. This work employs lattice parameter descriptors to develop a hybrid genetic algorithm (GA) and support vector regression algorithm (SVR) intelligent model for determining the energy gap of doped SnS semiconductors. The predictive strength of the developed GA-SVR model is compared with the stepwise regression algorithm- (STRA-) based model using different performance evaluation parameters. The developed GA-SVR model performs better than STRA model based on root mean square error, mean absolute error, and correlation coefficient with performance improvement of 70.68%, 67.63%, and 20.98%, respectively, using the testing set of data. Influence of different dopants and experimental conditions on energy gap of SnS semiconductor were investigated using the developed model, while the obtained values for the energy gaps agree with the measured values. The developed models demonstrate high degree of potentials in terms of accuracy, precision, and ease of implementation that fosters their real-life applicability in estimating the energy gap of doped SnS semiconductor with experimental stress circumvention.

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Gravitational Search Algorithm for Calculating Exciton Binding Energy in Monolayer Transition Metal Dichalcogenides

Oloore

Owolabi

2020

Journal of Elec Materi

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Support Vector Regression Ensemble for Effective Modeling of Magnetic Ordering Temperature of Doped Manganite in Magnetic Refrigeration

Cited by 7 publications

References 61 publications

Modeling the Maximum Magnetic Entropy Change of Doped Manganite Using a Grid Search-Based Extreme Learning Machine and Hybrid Gravitational Search-Based Support Vector Regression

Modeling the Maximum Magnetic Entropy Change of Doped Manganite Using a Grid Search-Based Extreme Learning Machine and Hybrid Gravitational Search-Based Support Vector Regression

Modeling Energy Gap of Doped Tin (II) Sulfide Metal Semiconductor Nanocatalyst Using Genetic Algorithm‐Based Support Vector Regression

Gravitational Search Algorithm for Calculating Exciton Binding Energy in Monolayer Transition Metal Dichalcogenides

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