scholarly journals Energy Band Gap Modeling of Doped Bismuth Ferrite Multifunctional Material Using Gravitational Search Algorithm Optimized Support Vector Regression

Crystals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 246
Author(s):  
Taoreed O. Owolabi ◽  
Mohd Amiruddin Abd Rahman
Keyword(s):  
Band Gap ◽  
Support Vector Regression ◽  
Energy Band ◽  
Lattice Distortion ◽  
Search Algorithm ◽  
Bismuth Ferrite ◽  
Gravitational Search Algorithm ◽  
Support Vector ◽  
Energy Band Gap ◽  
Gravitational Search

Bismuth ferrite (BiFeO3) is a promising multiferroic and multifunctional inorganic chemical compound with many fascinating application potentials in sensors, photo-catalysis, optical devices, spintronics, and information storage, among others. This class of material has special advantages in the photocatalytic field due to its narrow energy band gap as well as the possibility of the internal polarization suppression of the electron-hole recombination rate. However, the narrow light absorption range, which results in a low degradation efficiency, limits the practical application of the compound. Experimental chemical doping through which the energy band gap of bismuth ferrite compound is tailored to the desired value suitable for a particular application is frequently accompanied by the lattice distortion of the rhombohedral crystal structure. The energy band gap of doped bismuth ferrite is modeled in this contribution through the fusion of a support vector regression (SVR) algorithm with a gravitational search algorithm (GSA) using crystal lattice distortion as a predictor. The proposed hybrid gravitational search based support vector regression HGS-SVR model was evaluated by its mean squared error (MSE), correlation coefficient (CC), and root mean square error (RMSE). The proposed HGS-SVR has an estimation capacity with an up to 98.06% accuracy, as obtained from the correlation coefficient on the testing dataset. The proposed hybrid model has a low MSE and RMSE of 0.0092 ev and 0.0958 ev, respectively. The hybridized algorithm further models the impact of several doping materials on the energy band gap of bismuth ferrite, and the predicted energy gaps are in excellent agreement with the measured values. The precision and robustness exhibited by the developed model substantiate its significance in predicting the energy band gap of doped bismuth ferrite at a relatively low cost while the experimental stress is circumvented.

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

Crystals ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 36
Author(s):  
Olusayo Olubosede ◽  
Mohd Amiruddin Abd Rahman ◽  
Abdullah Alqahtani ◽  
Miloud Souiyah ◽  
Mouftahou B. Latif ◽  
...  
Keyword(s):  
Energy Harvesting ◽  
Band Gap ◽  
Zinc Selenide ◽  
Energy Band ◽  
Performance Metrics ◽  
Optical Filters ◽  
Optical Recording ◽  
Light Energy ◽  
Support Vector ◽  
Energy Band Gap

Zinc selenide (ZnSe) nanomaterial is a binary semiconducting material with unique features, such as high chemical stability, high photosensitivity, low cost, great excitation binding energy, non-toxicity, and a tunable direct wide band gap. These characteristics contribute significantly to its wide usage as sensors, optical filters, photo-catalysts, optical recording materials, and photovoltaics, among others. The light energy harvesting capacity of this material can be enhanced and tailored to meet the required application demand through band gap tuning with compositional modulation, which influences the nano-structural size, as well as the crystal distortion of the semiconductor. This present work provides novel ways whereby the wide energy band gap of zinc selenide can be effectively modulated and tuned for light energy harvesting capacity enhancement by hybridizing a support vector regression algorithm (SVR) with a genetic algorithm (GA) for parameter combinatory optimization. The effectiveness of the SVR-GA model is compared with the stepwise regression (SPR)-based model using several performance evaluation metrics. The developed SVR-GA model outperforms the SPR model using the root mean square error metric, with a performance improvement of 33.68%, while a similar performance superiority is demonstrated by the SVR-GA model over the SPR using other performance metrics. The intelligent zinc selenide energy band gap modulation proposed in this work will facilitate the fabrication of zinc selenide-based sensors with enhanced light energy harvesting capacity at a reduced cost, with the circumvention of experimental stress.

An efficient intrusion detection technique based on support vector machine and improved binary gravitational search algorithm

2019 ◽  
Vol 53 (5) ◽  
pp. 3255-3286 ◽  
Author(s):  
M. R. Gauthama Raman ◽  
Nivethitha Somu ◽  
Sahruday Jagarapu ◽  
Tina Manghnani ◽  
Thirumaran Selvam ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Intrusion Detection ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Detection Technique ◽  
Support Vector ◽  
Gravitational Search

scholarly journals Barium Titanate Semiconductor Band Gap Characterization through Gravitationally Optimized Support Vector Regression and Extreme Learning Machine Computational Methods

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Sunday O. Olatunji ◽  
Taoreed O. Owolabi
Keyword(s):  
Barium Titanate ◽  
Band Gap ◽  
Support Vector Regression ◽  
Extreme Learning Machine ◽  
Energy Gap ◽  
Gravitational Search Algorithm ◽  
High Rate ◽  
Percentage Error ◽  
Support Vector ◽  
Learning Machine

Barium titanate (BaTiO3) is a class of ceramic multifunctional materials with unique thermal stability, prominent piezoelectricity constant, excellent dielectric constant, environmental friendliness, and excellent photocatalytic activities. These features have rendered barium titanate indispensable in many areas of applications such as electromechanical devices, thermistors, multilayer capacitors, and electrooptical devices. The photocatalytic activity of barium titanate semiconductor is hindered by its large band gap and high rate of charge recombination. Doping of the parent barium titanate compound for band gap tuning is challenging and consumes appreciable time and other valuable resources. This present work relates the influence of foreign material incorporation into the parent barium titanate with the corresponding energy band gap by developing extreme learning machine- (ELM-) based models and hybridization of support vector regression (SVR) with gravitational search algorithm (GSA) using the structural lattice distortion that emanated from doping as model descriptors. The developed gravitationally optimized SVR (GSVR) is characterized with a low value of mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) of 0.036 ev, 1.145 ev, and 0.122 ev, respectively. The developed GSVR model outperforms ELM-Sine and ELM-Sig models using various performance evaluators. The developed GSVR model investigates the significance of iodine and samarium incorporation on the band gap of the parent barium titanate and the attained energy gaps conform excellently to the experimentally reported values. The demonstrated precision of the developed GSVR as measured from the closeness of its estimates with the measured values provides a quick and accurate method of energy gap characterization with circumvention of experimental stress and conservation of valuable time as well as other resources.

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