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

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